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Scientific Reports volume 16, Article number: 17416 (2026) Cite this article
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Accumulation of dust on solar panels lowers performance and limits energy production, particularly in dry locations. Dust accumulation on photovoltaic panels diminishes performance and reduces energy output, especially in arid regions. This study uses four identical modules in Roorkee, India, from October to December to examine the impact of cleaning frequency on photovoltaic (PV) performance. The reference panel is cleaned daily, while the remaining panels are cleaned weekly, biweekly, and monthly. Alongside short-circuit current measurements, environmental parameters including global horizontal irradiance, ambient temperature, wind speed, and relative humidity are continuously recorded. In this study, soiling loss (%) is examined as the primary performance indicator under various cleaning intervals to observe dust accumulation progression and its impact on the performance of the solar photovoltaic module. Experimental data are utilized to develop an empirical regression model that describes the trend of dust accumulation. The daily average soiling loss ranges between 0.17 and 0.21%. Furthermore, machine learning models, including Decision Tree, K-Nearest Neighbour, support vector regression, artificial neural network, and a stacking ensemble method, are developed for accurate prediction of soiling loss from environmental variables and cleaning frequency. The stacking model consistently achieves the best performance across all months, with root mean square error as low as 0.03–0.045, mean absolute error below 0.03, and R² = 0.999 compared to other models. Moreover, statistical analyses such as Bland–Altman plots and the Wilcoxon signed-rank test are employed to validate the significance and agreement of the predicted outcomes. The study highlights the benefits of data-driven solutions for predictive operation and maintenance of solar photovoltaic systems and provides valuable insights into the impact of cleaning frequency on reducing soiling losses.
The solar energy extensively uses for heating purpose, desalination of water, cooling process and production of electrical energy, serving a diverse array of applications from home to industrial as well in agricultural sectors^1,2. About half of the world’s electricity will come from wind and solar power alone by 2050³. Until then, around two-thirds of India’s electricity will come from solar and wind. The price of solar energy has dropped by around 85% since 2010⁴. The primary factors for India’s solar PV to develop exponentially are the sharp decline in solar energy prices and Indian government policies subsidies schemes to use solar PV technology to produce power. India has abundant solar insolation due its location inside the tropical belt, enabling it to fulfil its daily electrical requirements. It gets an average solar insolation of 4 to 7 kWh/m² and around 2300 to 3200 h of sunlight annually⁵. Numerous components affect the efficiency of solar photovoltaic power generating systems as shown in Fig. 1 such as type of material, spacing of solar cell, module area, tilt angle and orientation, environmental condition, surface dust of solar photovoltaic (PV) panels. The most often occurring element influencing the solar photovoltaic panel performance is surface dust^6,7,8. The accumulation of dust on the surface of solar panels can result in changes in the electrical charecteristics of the panel array. These changes can cause the panels to have a reverse bias, which in turn can result in a loss of power generated by the panels⁹. The synthesized bio-derived TiO₂ nanoparticles using plant extract and demonstrated improved photovoltaic performance through enhanced light absorption and charge transport, highlighting the importance of material-level enhancements for solar cell efficiency¹⁰.The soiling rates vary between 0.05% and 0.55% per day in India^11,12, while in Dhaka, Bangladesh, it is 0.78%¹³.
Factors contributing dust accumulation impacts on PV modules.
Large-scale solar farms in remote locations are particularly affected by the soiling issue. This is due to the fact that regular cleaning and inspection may be challenging and costly, given the expenses of labor and long-distance travel^14,15. In order to determine the amount of power that is lost by dirty photovoltaic modules, it is desirable to have automated soiling detection. Inadequate maintenance of the cleanliness of solar photovoltaic panel surfaces will lead to significant economic losses. Consequently, utilising precise and effective techniques to identify dust buildup on the surfaces of solar photovoltaic panels is crucial. This facilitates prompt cleaning of the panels, thereby ensuring their safe and efficient functioning. Dust and ambient temperature energy losses were quantified using artificial neural network (ANN) and extreme learning machine (ELM) algorithms¹⁶. Both the ELM and ANN models predict 91.42 and 90.69% accurately. Multivariate Linear Regression (MLR) and ANN models were used to estimate dust-related energy and economic losses in solar panels¹⁷. ANN and MLR models estimated dust-related cost and energy losses at 89.97% and 86.78%, respectively. In another study Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict the dust-exposed solar module performance¹⁸. The ANFIS model achieves root mean square error (RMSE) of 0.18719 and coefficient of determination (R²) of 0.99803 for monocrystalline silicon PV modules. In comparison, polycrystalline PV modules have an RMSE of 0.87098 and a R² of 0.99714. The study in¹⁹ predicted dust-induced PV panel performance deterioration in Qatar using ANN and MLR models. The ANN model has a R² of 0.537 and mean square error (MSE) of 0.0038, while the MLR model has a R² of 0.167 and an MSE of 0.0082. The ANN model outperformed the MLR model. Study in²⁰ estimated dust losses using artificial neural networks. Modern technology allows ANN to estimate losses with normalized root mean square errors (NRMSE) of 6.79 and correlation coefficients (R) of 0.91. Another of²¹ utilised AMM, MLR, Interactive Multivariate Linear Regression Model (MLRWI) and Response Surface Methodology (RSM), to predict the loss caused by dust on solar PV module surface. The artificial neural network produced better predictive results than the other machine learning models. The results for R² and RMSE are 0.813 and 0.026, respectively. The separate studied carried out focusing on machine learning (ML) methods implemented and their performance as shown in Table 1.
The studies summarised in Fig. 2 show that the reported soiling losses vary widely with location, exposure duration and local climatic conditions —observed rates in the literature span roughly 0.1% to 1.1% perday, with the highest values typically found in arid, dusty environments (e.g. Bahrain, Qatar) and lower values in regions with occasional rainfall or wind cleaning. Differences in measurement period, panel tilt, dust type, and cleaning practice (and the diversity of experimental protocols) make direct comparison difficult. Overall, the literature indicates a clear need for (a) longer-term, standardized measurements across diverse climates, (b) studies that relate soiling loss to measured environmental drivers (global horizontal irradiance (GHI), wind speed (WS), relative humidity (RH), dust deposition rate (DDR)), and (c) predictive models ( ML approaches) validated against controlled experiments^{36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55}. These gaps motivate the present work, which combines experimental measurements with ML models to produce robust soiling-loss predictions.
Real-world natural dust build up on PV panels under four cleaning frequencies (daily, weekly, biweekly, monthly) is studied instead of controlled dust deposition research, offering practical insights.
This work established two empirical models: one for PV short circuit current (I_sc) prediction and one for soiling loss (SL), encompassing both electrical performance and soiling effects.
Novel stacking model for SL prediction and comparison with other ML models (ANN, SVM, KNN, DT) provide strong empirical and data-driven comparisons.
Bland–Altman analysis and the Wilcoxon signed-rank test for model validation provide a unique level of statistical validity, assuring model dependability.
Soiling loss and study duration across locations.
The methodology of the present work is shown in Fig. 3 with an experimental setup consisting of four PV panels subjected to different cleaning frequencies (P1-daily, P2-weekly, P3-biweekly, and P4-monthly). Data including GHI, RH, WS, ambient temperature (AT), and the short-circuit current (Isc) of each panel were recorded using a data logger. Based on the collected data, two empirical models were developed: An Isc model as a function of environmental parameters such as global horizontal irradiance (GHI), ambient temperature (AT), wind speed (WS), relative humidity (RH) are considered in present study, and a soiling loss (SL) model as a function of RH, WS, AT, and cleaning frequency (CF). To improve predictive capability, machine learning models (ANN, SVM, KNN, DT, and stacking ensemble) were implemented for SL prediction. The performance of empirical and ML models was compared using evaluation metrics RMSE, mean absolute percentage error (MAPE), MAE, MSE, and R². The statistical validation using Bland–Altman analysis and the Wilcoxon signed-rank test was carried out to assess the significance and agreement of the models.
Methodology of the work.
The experimental work was conducted in Roorkee, India (29.86°N, 77.89°E), located in the Indo-Gangetic plain and characterised by a subtropical climate with distinct winter, summer, and monsoon seasons. The study period, October to December, represents the dry-winter season with frequent dust-laden winds, moderate humidity, and occasional foggy conditions, making it suitable for investigating soiling effects. Rainfall-induced natural cleaning was avoided to maintain experimental consistency and isolate the effects of environmental variables and manual cleaning intervals.
The experimental setup is shown in Fig. 4, consisted four identical crystalline silicon PV modules, each rated at the same electrical capacity (:{P}_{max}) 20 W, installed outdoors with a fixed tilt angle of 30⁰ and south-facing orientation to maximize solar exposure. All the PV modules are mounted on a common frame to ensure that they experienced identical environmental conditions such as GHI, AT, RH, and WS.
The present study isconducted during the dry season because the primary objectiveis to evaluate the impact of manual cleaning at fixed and predefined intervals (daily, weekly, biweekly, monthly) under controlled accumulation conditions. During the monsoon season, frequent rainfall events act as natural cleaning mechanisms. Such stochastic and uncontrolled cleaning would interfere with the predefined manual cleaning schedules and compromise the controlled comparison between different cleaning frequencies.
Experimental setup: (1–4) PV modules with different cleaning frequencies, (5) weather station, (6) data logger, and (7) PV analyser.
The data acquisition architecture shown in Fig. 4 as follows:
The ATMEGA2560-based data logger is shown measuring:
Global horizontal irradiance (GHI).
Ambient temperature (AT).
Relative humidity (RH).
PV module current and voltage.
Timestamp via real-time clock (RTC).
The Weather Station is separately indicated as the source of:
Wind Speed (WS).
The experiment is conducted under natural outdoor exposure, allowing dust to accumulate under real environmental conditions. Although dust composition may vary geographically, the measured electrical performance and environmental parameters inherently capture the net effect of dust deposition and adhesion. This approach ensures that the dataset reflects realistic soiling behaviour and provides a reproducible foundation for predictive modelling.
To evaluate the impact of cleaning frequency on soiling losses, four panels are exposed to several cleaning regimens. Panel P1 served as the clean reference and underwent daily cleaning, whilst P2, P3, and P4 were cleaned weekly, biweekly, and every four weeks, respectively. Cleaning occurred at 06:00 AM utilizing distilled water and a gentle, lint-free cloth to avert scratches or more surface contaminants. Electrical measurements encompassed the short-circuit current of each module, recorded at consistent intervals as the principal performance metric for dust build-up. Meteorological parameters were continually recorded, including global horizontal irradiance, ambient temperature, relative humidity, and wind speed. The data were obtained using calibrated sensors incorporated with a data logging system, ensuring synchronous environmental and electrical recordings. The experimental configuration included several sensors to enable precise data collection and real-time observation of solar PV system metrics presented in Table 2.
Electrical and environmental parameters (GHI, AT, RH, WS, (:{I}_{SC}), and voltage) were recorded at fixed and uniform intervals using the ATMEGA2560-based data acquisition system. Measurements were sampled at regular intervals (hourly), ensuring temporal consistency throughout the experimental period. For modeling analysis, the recorded data were aggregated into daily average values to represent the cumulative effect of dust deposition under each cleaning frequency. The Fig. 5 illustrates the cleaning schedule followed during the experimental period from October to December, clearly highlighting the systematic maintenance intervals adopted for each panel.
Cleaning schedule timeline (Oct-Dec 2024).
To account for the influence of external climatic variables, meteorological parameters were continuously recorded during the experimental study. The Fig. 6 illustrates the daily average variation of global horizontal irradiance and wind speed, while Fig. 7 presents the daily average variation of relative humidity and ambient temperature from October to December 2024. Statistical description of the collected data is shown in Table 3. These measurements highlight the dynamic environmental conditions under which the PV panels operated, providing essential context for analysing the soiling effect and validating the empirical and machine learning models.
Daily average global horizontal irradiance and wind speed during the experimental period (October–December 2024).
Figure 6 shows that the wind speeds stayed in the moderate range (~ 1–3 m/s) during the experiment. Under such conditions, particle transport and deposition mechanisms are likely to dominate over aerodynamic removal, explaining the observed positive correlation between wind speeds and soiling loss.
Daily average relative humidity and ambient temperature during the experimental study (October–December 2024).
The abrupt increase in relative humidity as shown in Fig. 7 accompanied by a drop in ambient temperature corresponds to short-duration high-humidity events commonly observed during the winter season in the Indo-Gangetic Plain. These events are typically associated with fog formation, condensation, or transient cloud cover rather than measurable rainfall.
The monthly data analysis from October to December 2024 shows how the performance of PV is affected by both changes in the environment and how often it is cleaned. The daily-cleaned panel (P1) consistently exhibited higher short-circuit current values, while progressive reductions were observed in P2, P3, and most significantly in P4, reflecting the cumulative effect of soiling at longer cleaning intervals. The variability of irradiance, indicated by higher standard deviation values, further underscores the dynamic operating environment of the panels. These findings confirm that both environmental conditions and soiling accumulation substantially impact PV output, thereby establishing the need for predictive models. Accordingly, the subsequent section develops empirical models to express.
(:{I}_{SC:})as a function of GHI, AT, RH, and WS, and to quantify soiling loss as a function of RH, WS, AT, and CF, thus providing a foundational framework for later comparison with machine learning models.
The experimental dataset was utilised to derive empirical regression models for short-circuit current and soiling loss in percentage, using global horizontal irradiance, ambient temperature, wind speed, relative humidity, and cleaning frequency as predictors. Multiple linear regression is adopted to establish these relationships.
The regression Eq. (1) obtained for (:{I}_{SC:}) is:
Table 4 presents the estimated regression coefficients for the empirical model of short-circuit current⁵⁶. The results clearly highlight GHI as the most dominant predictor (Estimate = 0.001265, p < 0.001), consistent with the physical dependence of Isc on solar irradiance. Cleaning frequency also shows a highly significant negative effect (p < 0.001), indicating the reduction in current with increasing days between cleaning. Ambient temperature has a small but significant positive effect, while relative humidity shows a minor negative influence. In contrast, wind speed was statistically insignificant (p = 0.238), confirming its limited role in determining (:{I}_{SC:}).
To further ensure model robustness, adjusted R² and residual diagnostics were evaluated before and after removing GHI. The change in adjusted R² was negligible (< 0.001), confirming that irradiance does not contribute to predictive power in the SL formulation. The removal therefore improves model parsimony without compromising explanatory strength, consistent with regression theory principles. The regression Eq. (2) for soiling loss (SL) expressed as,
Table 5 presents the estimated regression coefficients for the empirical model of of soiling loss show that cleaning frequency is the most significant predictor (Estimate = 0.18487, p < 0.001), highlighting the strong impact of longer cleaning intervals on increased soiling losses. Relative humidity also exhibits a significant positive influence (p = 0.007), which may be attributed to dust adhesion and cementing effects under humid conditions. Ambient temperature has a significant negative effect (p < 0.001), suggesting that higher temperatures may reduce relative deposition or increase self-cleaning effects. Wind speed shows a near-significant positive influence (p = 0.048), reflecting its dual role in either removing or redistributing dust. In contrast, GHI is statistically insignificant (p = 0.989), indicating that irradiance itself does not directly drive soiling loss but instead affects PV output through (:{I}_{SC}).
To further ensure model robustness, adjusted R² and residual diagnostics were evaluated before and after removing GHI. The change in adjusted R² was negligible (< 0.001), confirming that irradiance does not contribute to predictive power in the SL formulation. The removal therefore improves model parsimony without compromising explanatory strength, consistent with regression theory principles.
The performance of the four PV modules was evaluated in terms of short-circuit current (Isc), soiling ratio (SR) and soiling loss (SL%). The SR and SL can be computed using I_SC as mention below Eqs. (3) and (4) as,
Where I_{sc soiled} is the short-circuit current of the soiled panel and I_{sc clean} is that of the clean reference panel (P1). The soiling loss percentage (SL%) was calculated as,
To further improve predictive accuracy beyond the empirical formulations, machine learning algorithms were employed using the experimental dataset described in Sect. 3. The empirical SL model achieved a high determination coefficient (R² = 0.978) with low RMSE and MAE; however, the mean absolute percentage error (MAPE = 28%) remained relatively high, reflecting systematic nonlinearities and residual bias. To address these limitations, an ML-based predictive framework was developed.
The experimental dataset consist of both environmental and operational parameters: global horizontal irradiance (GHI), ambient temperature (AT), wind speed (WS), relative humidity (RH), reference short-circuit current of the clean panel ((:{I}_{SC:left(Cleanright)})) and cleaning frequency (CF). Categorical variables such as cleaning interval were encoded as hot encoding method, while all continuous features were standardized to zero mean and unit variance. To assess the relative contribution of selected input variables, a sensitivity analysis using the CAM approach was carried out under the Sect. 2.4. The Fig. 8 presents the scatter matrix of the selected features (CF, GHI, AT, WS, RH, and SL), excluding the month variable. In contrast to the off-diagonal scatter plots, which represent the pairwise correlations between the parameters, the diagonal histograms illustrate the distribution of each parameter.
Pairwise matrix of experimental features and relationship with soiling losses.
Global horizontal irradiance, ambient temperature, relative humidity, and wind speed have substantial temporal autocorrelation, making subsequent measurements not statistically independent. Randomly mixing time-dependent samples destroys temporal structure and leaks temporal data, enabling the model to indirectly learn patterns from subsequent observations. This may result in inappropriately optimistic assessment outcomes and exaggerated performance measures (e.g., R²). To evade this, the dataset was divided into 80% training and 20% testing observations using a time-ordered split. This method retains temporal causality and provides realistic model generalization in practice.
Time-ordered data splitting for temporally correlated environmental data.
Figure 9 compares random and time-ordered (blocked) data splitting for temporally auto-correlated environmental variables (GHI, RH, AT, WS). Random splitting leaks temporal data and inflates performance measures by include samples from comparable time periods in practice and testing. Time-ordered splitting conserves chronology and enables realistic generalization.
In this work, the cosine amplitude method (CAM) was used to assess the correlation between the input and output data. The mathematical formulation of CAM given in Eq. (5).
There is a connection between the cosine function and the dot product, as shown by Eq. (3). Whereas the inner product of two vectors where Xi input vector while Y is the output vector equal to zero when they are at right angles to one another, the product of two vectors that are collinear is equal to one. A greater directional similarity (and hence sensitivity) to the output is seen by features that have higher CAM scores (closer to 1) than those with lower scores as shown in Fig. 10.
Cosine amplitude scores (CAM) for feature importance.
CAM is the cosine similarity between the features (GHI, AT, WS, RH and CF) and target vectors soiling loss (%) is scale-invariant. The Fig. 9 indicate that the CF is the strongest driver of soiling loss in comparison with meteorological variables which shows moderate CAM score.
To evaluate potential multicollinearity among environmental predictors, the Variance Inflation Factor (VIF) was computed for GHI, RH, and AT. The obtained VIF values were 1.023 (GHI), 1.5987 (RH), and 1.5809 (AT), all of which are substantially below the threshold value of 5. These results confirm the absence of significant multicollinearity and demonstrate that the regression coefficients are stable and not adversely affected by linear dependency among predictors.
To further validate feature sensitivity, permutation importance analysis⁵⁷ was performed, as shown in Fig. 11 Cleaning frequency was identified as the dominant predictor of soiling loss, consistent with physical dust accumulation mechanisms. Environmental variables such as ambient temperature, relative humidity, irradiance, and wind speed exhibited smaller but meaningful contributions. The inset plot provides a detailed view of environmental feature importance. These findings confirm the robustness and physical consistency of the predictive model.
Permutation-based feature importance analysis.
After pre-processing data, soiling loss are estimated using various method of ML such as DT, KNN, SVM, ANN and Stacking. The stacking ensemble combined ANN, SVM, and DT as base learners, with a gradient boosting regressor (GBR) as the meta-learner. GBR effectively refined the base predictions by capturing residual nonlinear patterns, resulting in improved accuracy and reduced bias. MATLAB R2024 a is used to run simulations on a Dell laptop, featuring a Core i9-11900 H processor and 32 GB of RAM.
ANN is intended to replicate the neuronal organisation of the human brain by employing layers that are interconnected in order to capture complicated interactions, as seen in (Fig. 12)^58,59. Backpropagation is used to train the model in this study in order to minimize the errors. In given Eq. (6) w_n are representing weights corresponding to each inputs x_n and b is the bias, while final predicted output represented by Y.
ANN model.
Figure 13 shows how support vector machine (SVM) uses kernel functions to divide data in high-dimensional regions and capture complicated connections for classification and regression⁶⁰. It estimates SL in this work and expressed in Eq. (7) as,
where, Z is the input vector, W is the weight and B is the bias term.
Support vector machine (regressor).
RT, as seen in Fig. 14, are decision trees used to forecast continuous variables SL by segmenting data according to defined criteria and computing the mean target value for each subgroup. They are interpretable, resilient to outliers, and adept at managing non-linear connections successfully⁶¹. The proposed approach employs regression trees by segmenting the feature space and predicting the target variables SL inside each segment as mentioned in Eq. (8).
Decision tree (regressor).
In a regression tree, N is the total number of nodes (leaves), each region Z_n represents a partition of the feature space, C_n is the mean target value within that region, and I (X∈Zm) is an indicator function that equals 1 if X belongs to Z_n otherwise 0.
The K-nearest neighbours (KNN) algorithm is a simple, non-parametric method that predicts outputs based on the average of the k closest data points in the feature space as shown in Fig. 15. In the context of this work, KNN estimates soiling loss by finding similar conditions of GHI, AT, WS, CF and RH from experimental data. It is intuitive and effective for capturing local patterns without requiring an explicit training phase.
K-nearest neighbours.
In KNN regression equation where K is the number of nearest neighbours, N_K(X) is the set of those neighbours, and Y_i​ are their target values, making the prediction the average of the K closest points.
The stacking model is an ensemble learning approach that combines multiple base learners to improve predictive performance shown in Fig. 16. In this work, ANN, SVM, and DT were used as base learners to capture diverse data patterns, and their outputs were blended by a Gradient Boosting Regressor (GBR) as the meta-learner. This framework leverages the strengths of each individual model while compensating for their weaknesses. As a result, the stacking model achieved higher accuracy and robustness compared to single-model approaches.
For the L base learner the stacking prediction given in Eq. (10) as,
where m_L (x) are the base learner outputs (ANN, SVM, DT in this work) and g(⋅) is the meta-learner (GBR) that combines them to produce the final output.
Stacking ensemble model.
In this study, conventional random k-fold cross-validation was not employed because the dataset represents a physically time-ordered environmental process. Environmental variables such as irradiance, temperature, humidity, and wind speed exhibit strong temporal autocorrelation and causal continuity. Randomized cross-validation would mix past and future observations, introducing information leakage and leading to overly optimistic performance estimates, particularly for stacking models where the meta-learner learns second-order correlations. To ensure physically realistic and leakage-free validation, a time-ordered training–testing strategy was adopted. As illustrated in the Fig. 17, the dataset is divided chronologically into a training window (earlier observations) and a testing window (later unseen observations). The base learners (ANN, SVM, and DT) were trained exclusively on the training window, and their predictions were used to train the meta-learner (Gradient Boosting Regressor). The trained stacking model was then evaluated only on the testing window, which contained future unseen samples.
Time-aware training of stacking ensemble without cross-validation leakage.
The hyperparameters of all machine learning models, including ANN, SVM, DT, KNN, and the GBR used in the stacking ensemble, were selected using a systematic tuning procedure based on grid search combined with validation on the training dataset⁶². The hyperparameter combinations presented in Table 6 which is used to all machine learning model in order to minimized prediction error while avoiding overfitting. For each model, a range of candidate hyperparameters was evaluated, and the optimal configuration was selected based on minimum RMSE and stable generalization performance. The same training dataset and evaluation criteria were applied consistently across all models to ensure fair comparison.
It is important to evaluate the precision of the prediction model. A variety of measures have been used to evaluate the precision of predicting PV output power production¹², which include:
(a) Mean Absolute Error (MAE): Computes the average of absolute differences between actual and predicted values, giving equal weight to all errors as expressed in Eq. (11)
(b) Mean Square Error (MSE): Measures the average of squared differences between actual and predicted values, penalizing larger errors more, its mathematical expression mention in Eq. (12)
(c) Root Mean Square Error (RMSE): Square root of MSE, expressing as Eq. (13) prediction error in the same units as the target variable.
(d) Coefficient of Determination (R²): Indicates how much variance in the actual data is explained by the model, with values closer to 1 showing better fit.The expression shown below in Eq. (14)
(e) Mean Absolute Percentage Error (MAPE): Represents as shown in Eq. (15) the average absolute error as a percentage of actual values, useful for relative accuracy.
This section discusses the performance and findings of the suggested models. The testing findings under actual environmental condition from the Roorkee area, India, are also given according to month and cleaning frequency. The empirical model produced from the experimental data is constructed and compared with machine learning models.
The performance of the four PV modules was evaluated in terms of short-circuit current (Isc), soiling ratio (SR), soiling loss (SL%), and current–voltage (I–V) and power–voltage (P–V) characteristics. The daily-cleaned panel (P1) was taken as the clean reference, while P2, P3, and P4 represent panels cleaned at weekly, biweekly, and monthly intervals, respectively.
Short-circuit current ((:{I}_{SC:})) was adopted as the primary soiling indicator because dust accumulation predominantly reduces optical transmission, directly affecting photocurrent generation. Since Isc is approximately proportional to irradiance, it provides a linear and direct measure of optical attenuation. In contrast, power output (P_max) incorporates nonlinear temperature and fill factor effects, which may obscure pure dust-related losses.
In addition to (:{I}_{SC:})based metrics, I–V and P–V curves were generated using a calibrated PV analyzer for each panel at different cleaning intervals. These curves provide detailed insight into the effect of dust accumulation not only on the short-circuit current but also on the maximum power point (.
(:{P}_{MPP})), open-circuit voltage ((:{V}_{oc})), and fill factor (FF). Figures 18 and 19 shows daily average.
(:{I}_{SC:}) and daily soiling ratio (SR) trend over the time period of the experiment while Fig. 20 illustrate about the monthly soiling loss in percentage.
Daily average variation of short-circuit current for PV panels with different cleaning frequencies.
Daily variation of soiling ratio for PV panels cleaned at different intervals.
Monthly average soiling loss (%) for PV panels with different cleaning intervals: P2 (clean weekly), P3 (clean biweekly), and P4 (clean monthly).
The Figs. 18, 19 and 20 collectively illustrate the impact of cleaning frequency on PV performance. The daily-cleaned panel (P1) maintained the highest (:{I}_{SC:}), while P2–P4 showed progressive reductions with longer cleaning intervals. The soiling ratio (SR) exhibited a stepwise decline within each cleaning cycle, steepest for the monthly-cleaned panel (P4). Monthly average soiling losses confirmed this trend, increasing from 1 to 1.5% (P2) to 2.5% (P3) and5% (P4). These results clearly demonstrate that extended cleaning intervals accelerate dust-induced performance degradation.
PV analyser measurement of I-V and P-V characteristics at GHI 415 w/m² (a) P1:-clean daily (b) P2:- clean weekly (c) P3:- clean biweekly (d) P4:- clean monthly.
Figure 21 shows the I–V and P–V characteristics of the four PV panels under different cleaning frequencies. The clean reference panel (P1, cleaned daily) achieved the highest maximum power point ((:{P}_{MPP}) = 6.00 W) and current at MPP ((:{I}_{mpp}) = 0.370 A). Panels with reduced cleaning frequency demonstrated progressive reductions in both (:{I}_{mpp}) and (:{P}_{MPP}): P2 (weekly) produced 5.80 W, P3 (biweekly) dropped to 5.59 W, and P4 (monthly) showed the lowest performance at 5.31 W. The open-circuit voltage ( (:{V}_{oc})) remained relatively stable across all panels, indicating that dust accumulation primarily impacts the short-circuit current and the maximum power output.
Validation performance of empirical (:{I}_{SC:}) models for four PV panels under different cleaning frequencies during October–December are shown in Fig. 22 as, (a) RMSE, (b) MAE, and (c) MAPE. Panels P1–P3 (daily, weekly, and biweekly cleaning) maintained low errors (RMSE ≤ 0.009 A, MAE ≤ 0.007 A, MAPE = 1–1.5%), whereas P4 (monthly cleaning) exhibited significantly higher deviations (RMSE up to 0.020 A, MAE = 0.017 A, MAPE = 3.7% in December), highlighting the negative impact of extended cleaning intervals on model accuracy.
Monthly cleaning frequency wise performance evaluation of empirical model (a) RMSE (b) MAE (c) MAPE.
Figure 23 shows that the four PV panels have a R² value of 0.99 or above, proving that the empirical modelling framework is reliable for describing the changes in I_(SC) under various cleaning conditions. The gradual decline in R² with reduced cleaning frequency highlights the sensitivity of empirical models to dust accumulation patterns.
Experimental vs. Empirical model R-Squared plot (Oct-Dec 2024) of (a) P1:-clean daily (b) P2:- clean weekly (c) P3:- clean biweekly (d) P4:- clean monthly.
The three PV panels are compared from October to–December to analyse the measured vs. projected soiling loss (SL, %) as shown in Fig. 24. The estimated regression line explains most of the variation (monthly R² =0.97–0.98) with minor absolute errors (RMSE = 0.35, MAE = 0.27). The moderate relative error (MAPE = 26–31%) suggests systemic bias or nonlinear effects that the basic empirical fit cannot capture. The following part uses machine-learning to lessen this relative inaccuracy.
Experimental vs. empirical SL model R-squared plot for month (a) October (b) November (c) December.
The empirical SL model’s performance is shown in Fig. 25. PV Panel (a) displays the regression plot between actual and expected soiling loss values. The data points closely correspond with the fitted regression line, indicating a high coefficient of determination (R2 = 0.978). The model’s low RMSE (0.364) and MAE (0.278) validate its trend capture. However, the mean absolute percentage error (MAPE) remains greater (28%), showing relative variances, especially for lower SL values. Panel (b) shows the residual distribution, where errors are centred around zero but include outliers. This residual spread shows systematic deviations not completely represented by the empirical formulation, motivating the upcoming section to use sophisticated machine-learning algorithms to minimize relative error while maintaining high R².
Overall empirical SL model plot of (a) R-squared (b) residual.
Although the empirical SL model achieved a high coefficient of determination (R² ≈ 0.978), the MAPE value (~ 28%) appears relatively high. This is primarily due to the sensitivity of MAPE to small denominator values. Since several SL observations fall within low ranges (below 2%), even small absolute deviations lead to inflated percentage errors. Furthermore, the linear regression framework may not fully capture nonlinear dust accumulation patterns, contributing to structural bias at low SL levels. This constraint led to the introduction of machine learning algorithms to make percentage-based predictions more accurate. .
Experimental data was used to develop the machine learning model. The model inputs are AT, GHI, RH, WS, and CF, while the target variable is solar PV module SL. The prediction data size was 5 × 336 and divided 80:20 for training and testing, as shown in Table 7. SL is predicted using stacking, ANN, SVM, DT, and KNN models.
Statistical metrics are needed to assess machine learning models’ prediction performance for reliability and robustness. This research evaluated solar panel soiling loss models using MAE, RMSE, MAPE, and R2. These measures show the models’ capacity to reduce prediction errors, capture data variability, and generalize across environmental conditions.
The stacking ensemble model’s tight alignment of projected and observed responses in training and testing datasets showed high predictive performance in Fig. 26. The Fig. 27 shows residual plots with random residuals around zero, confirming the model’s dependability and lack of systematic bias. Performance metrics as shown in Table 8, which demonstrated the model’s resilience, with R² values of 0.9995 (training) and 0.9997 (testing) and low error values (RMSE: 0.0566 and 0.0456; MAE: 0.0404 and 0.0333). The stacking model generalizes effectively across datasets and outperforms individual models, making it a very accurate soiling loss prediction framework.
R² plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
To examine whether cleaning frequency (CF) dominates the learning process, a feature ablation study was conducted by evaluating models trained using (i) the full feature set, (ii) CF alone, and (iii) environmental variables alone.
The full model consistently achieved the lowest prediction error. Although CF-only models exhibit strong correlation with soiling loss due to their causal relationship, they produce significantly higher absolute errors compared to the full model. Conversely, models trained exclusively on environmental variables perform poorly. Table 9 shows that environmental characteristics give important extra information and that cleaning frequency does not hide environmental learning.
The scatter plots reveal that the ANN model accurately predicted soiling loss as shown in Fig. 28. The stacking model had somewhat less departures from the ideal prediction line than the ANN, especially at higher response levels. The Fig. 29 residual plots reflect this tendency, with residuals spreading more broadly and displaying patterns at extreme values, indicating small bias in specific ranges. Performance measurements is shown in Table 10, which supports this result, with R² values of 0.9923 (training) and 0.9806 (testing) and greater error levels (RMSE: 0.2138 and 0.2822; MAE: 0.1277 and 0.1358). The ANN model is highly predictive, but its error distribution and somewhat lower accuracy than the stacking model suggest it cannot completely capture nonlinear data variability.
R² plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
Compared to the ANN and stacking models, the DT model predicted well but had lesser accuracy. The scatter plot (Fig. 30) demonstrates that although projected responses track the actual values, deviations from the ideal prediction line are greater at higher response levels. The Fig. 31 shows residual plots with larger dispersion and predictable patterns, showing overfitting in specific areas. This is supported by performance measurements (Table 11), including R² values of 0.9767 (training) and 0.9777 (testing), and higher error levels (RMSE: 0.3766 and 0.4020; MAE: 0.1989 and 0.2090). The DT model captures the input-soiling loss connection, but its restricted generalization and higher residual spread make it less suitable than sophisticated ensemble approaches.
R² plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
The scatter plots are shown in Fig. 32 to show that the Support Vector Machine (SVM) model predicted values that matched observed responses. Significant departures from the ideal prediction line, especially at higher response levels, imply limits in catching extreme instances. The Fig. 33 residual plots show hetero-scedasticity in predictions, with errors spreading further at higher response levels. Performance measures (Table 12) indicate lower R² values (0.9527) and greater error values (RMSE: 0.5365 and 0.4418; MAE: 0.3124 and 0.2917) compared to ANN and stacking. SVM has superior generalization and testing performance than DT, but its lower accuracy and higher residual spread restrict it compared to the stacking ensemble.
R² plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
As seen in the scatter plots (Fig. 34), the k-Nearest Neighbor (kNN) model had mixed predictive performance, with projected values following the genuine responses but deviating at higher response levels. The Fig. 35 residual plots show higher error dispersion and systematic bias in extreme ranges, indicating model resilience is lowered. Performance measures (Table 13) demonstrate high generalization on test set but poor fit during training, with R² values of 0.7662 (training) and 0.9701 (testing). Our error measurements were greater, with RMSE values of 1.1926 (training) and 0.4657 (testing) and MAE values of 0.6948 and 0.3904. Despite good testing accuracy, the kNN model’s large training error and residual spread overfit local patterns and impair dependability compared to stacking.
R² plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
The slightly higher testing R² compared to training R² for the KNN model is attributed to the local interpolation nature of KNN and the distribution of samples in feature space, rather than data leakage. Since testing samples fall within well-represented regions of the training feature space, stable prediction performance is achieved.
To assess potential overfitting and validate the generalization capability of the proposed stacking ensemble model, learning curve analysis is performed in accordance with statistical learning theory. The training and validation errors were evaluated as a function of increasing training data size. The learning curves demonstrate that although the training error decreases with increasing sample size, the validation error converges to a stable and closely aligned value without divergence. The narrow gap between training and validation errors confirms that the stacking model does not suffer from overfitting and generalizes well to unseen data.
The ensemble structure successfully balances the bias-variance trade-offs, so the validation error does not increase as the model capacity increases. These results provide theoretical and empirical evidence that the high R² values achieved by the stacking model are due to robust learning rather than memorization of the experimental dataset.
Learning curve for training and testing.
Also, the fact that the validation error doesn’t go up when the model capacity goes up shows that the ensemble structure does a good job of balancing bias and variation. Learning curves showing in Fig. 36 convergence of training and testing RMSE for the stacking ensemble, indicating strong generalization and absence of overfitting.
To assess the robustness of the stacking model against potential measurement noise, controlled Gaussian perturbations (± 3%) were introduced to environmental input variables. The model was retrained using the same train–test partition to ensure consistency. Figure 37 illustrates the residual distribution comparison between the original inputs and perturbed inputs.
Residual distribution: original vs. noisy inputs (± 3%).
Model accuracy diminishes with longer cleaning intervals, with weekly cleaning reaching R² = 0.995 and low RMSE (between 0.05 and 0.1), whereas monthly cleaning drops R² to 0.964 and raises RMSE over 0.4, as shown in Fig. 38. In all intervals, the stacking model had the lowest error (e.g., MAPE < 10%, RMSE ≈ 0.05) and greatest R² (> 0.99), demonstrating its durability over individual models.
Model performance comparison (MAPE vs. RMSE, bubble ∝ R²) across cleaning frequency intervals (a) clean weekly (b) clean biweekly (c) clean monthly.
Figure 39 demonstrates that stacking had the lowest MSE at all cleaning intervals: 0.003 (weekly), 0.001 (biweekly), and 0.001 (monthly). Empirical and KNN models had the largest errors, 0.022–0.294 and 0.144–0.158, respectively, especially during longer cleaning intervals. These findings confirm that stacking provides the most accurate and consistent forecasts regardless of cleaning frequency.
Model MSE comparison across cleaning-frequency intervals (a) clean weekly (b) clean biweekly (c) clean monthly.
Figure 40 shows MAE fluctuation by cleaning interval. The stacking model had the lowest MAE values (0.030 (weekly), 0.022 (biweekly), and 0.018 (monthly), whereas empirical and KNN models had the largest errors (0.468 and 0.337, respectively). This proves stacking’s prediction error-reducing ability under protracted soiling.
Model MAE Heatmap across cleaning intervals P2:- clean weekly P3:-clean biweekly P4:- clean monthly.
These findings show that stacking is the best accurate method for soiling loss estimate across cleaning frequencies and is resilient to increasing soiling buildup.
Table 14 indicates that stacking consistently outperformed other models with RMSE ranging from 0.03 to 0.045, MAE ≤ 0.03, and R² = 0.999 throughout all months. Empirical and KNN models had the largest errors (e.g., MAPE up to 35.38% and MAE > 0.4), especially in December, proving the stacking ensemble’s better resilience and dependability.
Shewhart control charts of RMSE for October–December 2024 forecasting models are shown in Fig. 41. Control charts, or Shewhart charts, provide performance data over time to determine control limits. The upper and lower control limits (UCL and LCL) set the permitted range of variation. Values over these limits indicate instability or unexpected swings. Central line (CL) shows process mean. The stacking model showed the most consistent projected accuracy throughout all months, with an average RMSE of 0.04 and tight control limits (UCL = 0.06, LCL = 0.01). ANN showed RMSE variation between 0.13 and 0.30 (mean 0.20), DT between 0.27 and 0.33 (mean 0.29), and KNN peaked at 0.49 (mean 0.38), suggesting greater variability. The stacking ensemble predicts well because of its low errors (Figs. 42 and 43) and process stability.
Shewhart control charts of RMSE for different predictive models (Oct–Dec 2024).
The Shewhart control chart of RMSE (Fig. 41 shows that prediction errors remain well within the statistical control limits across all months, confirming stable model performance and absence of instability due to non-stationarity. The empirical regression model also maintained consistent performance, with R² values between 0.97 and 0.98 across different months, further supporting the temporal robustness of the predictive framework. The environmental variables recorded during the experimental period exhibited natural variability while remaining within the same physical operating regime, enabling the model to learn stable relationships between environmental drivers and soiling loss. Since the models rely on physically meaningful predictors such as cleaning frequency, humidity, and irradiance, the learned relationships remain consistent over time. These results confirm that the predictive models demonstrate stable performance across different months without evidence of significant parameter drift or non-stationary.
Month-wise comparison of MAPE (%) across predictive models.
Month-wise comparison of MAE across predictive models.
The Shewhart control chart analysis shows that the stacking model predicts soiling loss with the lowest prediction errors and retains stability within restricted limits, making it the most dependable strategy⁶³.
To ensure that the superior performance of the stacking model was not merely a consequence of increased model flexibility, several safeguards were employed:
Independent testing evaluation (80:20 split) demonstrated that training and testing R² values were nearly identical (0.9995 vs. 0.9997), indicating strong generalization without overfitting.
Residual analysis showed random dispersion without systematic patterns.
Month-wise and cleaning-frequency-wise evaluations confirmed consistent performance across operational conditions.
Control chart stability analysis demonstrated low variance and stable error distribution across months.
These results collectively indicate that the improved performance of the stacking model arises from its ability to capture nonlinear environmental interactions rather than merely from increased complexity.
To evaluate the effectiveness of the proposed machine learning framework, its performance was compared with the semi-empirical regression model developed in Sect. 3.2 under identical validation conditions. The empirical model represents a physics-based baseline using environmental predictors such as irradiance, temperature, humidity, wind speed, and cleaning frequency. As shown in Table 12, the stacking ensemble achieved significantly lower prediction error (RMSE = 0.03–0.045, MAE ≤ 0.03) compared to the empirical model (RMSE = 0.348–0.386, MAE = 0.266–0.294). This represents an approximately 85–90% reduction in prediction error. These results demonstrate the superior predictive capability of the proposed machine learning framework in capturing nonlinear soiling dynamics compared to conventional semi-empirical models.
After performance assessment, statistical analysis verified machine learning model dependability and robustness. Although error measurements like RMSE, MAE, MAPE, and R² assess accuracy, they do not adequately resolve bias between estimated and actual soiling loss levels. A Bland–Altman (BA) plot was utilized to visually evaluate agreement, highlight recurring deviations, and indicate prevalent prediction error boundaries. A non-parametric Wilcoxon signed-rank test was employed to see whether predicted and actual values differed significantly. These graphical and inferential methods analyse model performance comprehensively.
To evaluate the statistical independence of the experimental observations, the autocorrelation function (ACF) of the soiling loss time series was analyzed, as shown in Fig. 44 Since environmental and PV performance data are collected sequentially, temporal autocorrelation may reduce the effective sample size and affect model validity⁶⁴.
The ACF results show that autocorrelation values decrease rapidly and remain within the 95% confidence bounds for most lags. Only short-term correlations are observed at very small lags, while longer lags exhibit negligible autocorrelation. This indicates weak temporal dependence and confirms that the observations are sufficiently independent for predictive modelling.
Furthermore, the natural variability in environmental parameters, including irradiance, temperature, humidity, and wind speed, along with different cleaning frequencies, ensured diverse operating conditions across samples. This variability further supports the effective independence of observations and validates the robustness of the machine learning models.
Autocorrelation function of daily-averaged soiling loss residuals.
The Bland–Altman (BA) study assessed the agreement between anticipated and actual soiling loss values. The BA plot shows bias and limitations of agreement, indicating systematic and random model prediction deviations, unlike traditional error measures. A lower bias value and narrower ranges of agreement imply that model predictions match data. This approach helps validate if machine learning models can reproduce experimental observations across operational circumstances.
The arrangement of dots around zero illustrates the degree of concordance between predictions and actual values, with tighter clustering near the red bias line signifying enhanced consistency. The dispersion within the limits of agreement (LoA) indicates the model’s variability, while outliers situated far beyond the LoA denote instances of inaccurate predictions, as depicted in Figs. 45 and 46, respectively.
.
Bland–Altman plot for (a) Empirical model (b) Stacking ML model (c) ANN (d) DT (e) SVM (f) KNN.
Model biases with limits of agreement (vertical lines).
The Fig. 47 shows the Wilcoxon signed-rank test comparing real and forecasted soiling loss values to assess the models’ predictive ability. A statistically insignificant result (p > 0.05) suggests that model predictions match experimental results, indicating model resilience. However, a significant finding (p < 0.05) indicates consistent disparities between projected and actual values. All models’ actual and expected soiling loss values were compared using the Wilcoxon signed-rank test. Despite having the lowest median difference (0.0016), the stacking model has a substantial p-value (p = 0.0083), demonstrating its capacity to capture tiny deviations with high consistency. The empirical (p = 0.593), decision tree (p = 0.276), and KNN (p = 0.428) models had non-significant p-values, indicating no statistically significant difference between their predictions and actual values.
Wilcoxon signed-rank test plot for (a) Empirical model (b) Stacking ML model (c) ANN (d) DT (e) SVM (f) KNN.
Even with strong numerical performance, ANN (p = 2.71e-42) and SVM (p = 7.4e-24) showed substantial discrepancies, suggesting systematic prediction errors. Stacking is the most reliable technique since it has minimum bias and statistically significant consistency, whereas empirical and tree-based approaches are equivalent but less robust. Although the Wilcoxon signed-rank test yielded a statistically significant p-value (p < 0.01) for the stacking model, indicating that the median difference between predicted and actual values is not exactly zero, the magnitude of this deviation was extremely small (≈ 0.001–0.002). Given the relatively large sample size, even minor deviations can become statistically significant. However, absolute error metrics (RMSE and MAE) remained very low, suggesting that the detected bias is negligible in practical terms. Therefore, the stacking model demonstrates high predictive accuracy with minimal practical bias rather than perfect agreement.
Bland–Altman analysis and Wilcoxon signed-rank test p-values vary because they employ different statistical methods. The Bland–Altman approach estimates the p-value using a paired t-test to see whether the mean difference (bias) between actual and predicted values is substantially different from zero. The Wilcoxon signed-rank test, on the other hand, tests if the median of the paired differences deviates considerably from zero without assuming normality. BA focuses on systematic bias in the mean, whereas Wilcoxon confirms median differences, therefore p-values may vary. Two methods give a more complete statistical assessment of model performance.
Although the dataset originates from a single geographical site and season, meaningful domain shifts exist within the data due to temporal variation in environmental conditions and operational variation in cleaning frequency. Figure 48 illustrates covariate distribution shifts of global horizontal irradiance across months, confirming changes in the input feature space.
Figures 49 and 50 further demonstrate that the stacking model maintains stable RMSE across temporally distinct months and across different cleaning frequencies. The consistency of predictive performance under these distributional and operational shifts indicates robust within-domain generalization rather than simple interpolation of identical conditions. While the present study does not claim cross-climate transferability, the proposed framework demonstrates strong robustness within the studied domain.
Covariate distribution shift of GHI across months.
Temporal domain shift evaluation of stacking model.
Operational domain shift evaluation of stacking model.
To statistically validate the observed performance dominance of the stacking ensemble, paired Diebold–Mariano tests were conducted on squared prediction error sequences. The results indicate as shown in Table 15 that the stacking model significantly outperforms ANN, DT, SVM, KNN, and empirical models, with DM statistics ranging from − 3.97 to − 12.08 and corresponding p-values well below 0.05.
The negative DM statistics confirm that the stacking model consistently yields lower prediction errors than competing models. These findings demonstrate that the superior performance of the stacking ensemble is statistically significant and not attributable to random variation.
This study investigated natural soiling on solar panels subjected to different cleaning protocols and developed empirical and machine learning models for predicting soiling loss. We employ experimental analysis and data-driven methods to test, evaluate, and predict how well PV systems will work when they are dirty in the real world. Ensemble learning outperforms empirical approaches in accuracy and robustness. The experimental study on four PV panels with different cleaning frequencies (daily, weekly, biweekly, monthly) confirmed that natural soiling significantly impacts PV performance, with higher losses under longer intervals.
This study contributes to the field by establishing a validated experimental–empirical–machine learning framework for real-world soiling prediction under controlled cleaning intervals.
The proposed stacking model significantly outperforms the semi-empirical baseline, demonstrating improved predictive accuracy and robustness under identical validation conditions.
Unlike purely simulation-based studies, the proposed approach is grounded in field measurements and incorporates temporal causality-aware validation, making it both scientifically rigorous and practically deployable.
The findings provide a reproducible methodology for future PV degradation studies and open avenues for intelligent, data-driven operation and maintenance optimization in solar energy systems.
Two empirical models were created one for (:{I}_{SC:})prediction (dominated by GHI) and another for Soiling Loss (SL) (mainly impacted by RH and cleaning frequency). The SL model has R² = 0.978, but a high MAPE = 28%, suggesting insignificant nonlinear effects.
Machine learning showed considerable increases, with the stacking ensemble obtaining the highest accuracy (R² = 0.9997, RMSE = 0.0456, MAE = 0.0333) and surpassing individual models (ANN, DT, SVM, KNN), among other models.
Model accuracy falls according to decreased cleaning frequency, but stacking remains strong (MSE ≤ 0.003, MAE < 0.03) even with monthly cleaning.
Statistical validation using Bland–Altman and Wilcoxon signed-rank tests confirmed stacking’s superiority, with minimal bias and narrowest limits of agreement, although minor statistically detectable differences were observed in some cases.
Overall, the integrated experimental–empirical–ML framework demonstrates that ensemble-based data-driven models can reliably predict soiling loss, enabling optimized maintenance scheduling and predictive O&M strategies for PV systems.
While prediction error differences may appear numerically small, their operational significance becomes substantial when translated into cumulative energy losses over extended periods. As shown in Table 3, soiling losses exceeding 5% were observed under extended cleaning intervals. Accurate prediction of soiling progression enables optimized maintenance scheduling, improving energy yield and reducing operational costs. The proposed model is developed based on short-term dry-season data, during which module surface properties are assumed constant. Long-term surface degradation, coating wear, and micro-roughness evolution may influence dust adhesion behaviour and soiling accumulation rates. Such effects represent gradual structural drift and would require multi-season or multi-year datasets for comprehensive modelling. Therefore, the current framework is primarily applicable to short- and medium-term predictive maintenance planning.
The present model was developed using data collected during dry environmental conditions to ensure controlled soiling accumulation. Extreme events such as rainfall-induced natural cleaning or dust storms introduce regime shifts that require representative training data for accurate prediction. Future work will incorporate multi-season datasets including rainfall and extreme environmental conditions to enhance model robustness and generalization capability.
Data is available based on request.
AdaBoost
Autoencoder
Artificial neural network
Ambient temperature
Backpropagation neural network
Cleaning frequency
Convolutional neural network
Cell temperature
Diffuse horizontal irradiance
Atmospheric pressure
Particulate matter (PM10 / PM2.5)
Relative humidity
Direct normal irradiance
Wind speed
Global horizontal irradiance
K-nearest neighbor
Linear regression
Long short-term memory
Mean absolute error
Mean absolute percentage error
Machine learning
Multilayer perceptron
Mean square error
Root mean square error
Recurrent neural network
Seasonal auto regressive integrated moving average with exogenous variables
Sunshine hour
Soiling loss
Soiling ratio
Support vector machine
Support vector regression
Random forest
RGB images of solar panels
Decision tree
Extreme learning machine
Gated recurrent unit
Extreme gradient boosting
Coefficient of determination
Current at maximum power point
Short-circuit current
Short-circuit current of clean reference panel
Short-circuit current of soiled panel
Power output of clean panel
Maximum power output
Power output of soiled panel
Temperature of dusty panel
PV module temperature
Voltage at maximum power point
Open-circuit voltage
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This Research was conducted with the financial support provided by UPES, Dehradun, India. The authors express gratitude to the Research & Development Department at UPES, Dehradun, Uttarakhand, India for their support under Grant Number UPES/R&D-SoAE/25062025/27.
Open access funding provided by Manipal University Jaipur.
Electrical Cluster, School of Advanced Engineering, UPES, Dehradun, 248007, India
Ashutosh Shukla & Rupendra Kumar Pachauri
Miyan Research Institute, International University of Business, Agriculture and Technology, Dhaka, 1230, Bangladesh
Rupendra Kumar Pachauri
UCRD & CSE-APEX, Chandigarh University, Mohali, Punjab, India
Ranjan Walia
Department of Electrical Engineering, Manipal University Jaipur, Jaipur, India
Vinay Gupta
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Ashutosh Shukla (AS): Conceptualization, Methodology, Writing – original draft, Software, Visualization. Rupendra Kumar Pachauri (RKP): Methodology, Data curation, Writing – review and editing, Supervision. Ranjan Walia (RW): Investigation, Writing – review and editing, Supervision. Vinay Gupta (VG): Investigation, software, Visualization, data analysis, Writing – review and editing.
Correspondence to Vinay Gupta.
The authors declare no competing interests.
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Shukla, A., Pachauri, R.K., Walia, R. et al. Machine learning-based prediction of soiling losses in photovoltaic modules under different cleaning frequencies: an experimental investigation. Sci Rep 16, 17416 (2026). https://doi.org/10.1038/s41598-026-45485-2
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Solar panels are believed to have caused a fire at a North Yorkshire hospital earlier this month, the fire service has said.
The Springwood unit at Malton Hospital, which provides mental health services for older people, went up in flames on 8 July, completely destroying the building.
Tees, Esk and Wear Valleys NHS Foundation Trust, which runs the 14-bed unit, said an investigation into the blaze was ongoing, with a full report expected in the coming weeks.
"However, North Yorkshire Fire and Rescue Service has established that the fire most likely started at one of the solar panels on the roof of the building," a trust spokesperson added.
"Due to the damage sustained to the solar panels during the fire, it has not been possible to determine any further details about the cause."
All 15 patients were safely evacuated from the ward during the incident and no injuries were reported.
The fire follows a similar blaze at a village primary school in Suffolk last year, in which solar panels were identified as the cause.
On the day of the fire, the school had been unoccupied and no casualties were reported.
In March, another primary school in Brantham caught fire due to a solar panel issue and last month, the roof of an Ipswich primary school went up in flames with the same cause identified.
As a result, about 80 schools in Suffolk switched off their solar panels as a precautionary measure.
Suffolk County Council told the BBC the fires were all believed to have had different causes, suggesting there was not a common fault with the panels, but the three fires originated around the units.
The spokesperson for Tees, Esk and Wear Valleys NHS Foundation Trust said the safety of staff and the people they cared for remained their priority.
"We have robust fire safety measures in place across our trust, including regular fire alarm testing and fire safety checks," they added.
"We can confirm that fire safety risk assessments are in place and current for all our trust buildings. This also applies to our electrical infrastructure and installations."
They confirmed fire safety systems had operated as they were supposed to at the Springwood unit when the fire started.
Further updates are expected from the trust as work at the site progresses.
Listen to highlights from North Yorkshire on BBC Sounds, catch up with the latest episode of Look North.
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“We really want to be a resource to guide people through this major investment.”
Photo Credit: iStock
Across the Denver metro area, a new rebate may help households and businesses lower the cost of trading older furnaces and air conditioners for a cold-climate heat pump.
According to The Colorado Sun, the Denver Regional Council of Governments has launched a $1,500 Power Ahead Colorado rebate for qualifying heat pump installations.
The incentive is part of a $40 million effort spread over four years to cut emissions from buildings across the region.
Homeowners, businesses, and multifamily properties in Adams, Arapahoe, Boulder, Broomfield, Clear Creek, Denver, Douglas, Jefferson, Gilpin, and part of Weld County can use the program when installing a new heat pump or replacing an existing furnace or AC unit with one.
To qualify, the system must meet Energy Star standards and be installed by a Power Ahead Colorado-approved contractor.
Robert Spotts, who manages the program, said, “Buildings are the largest source of climate pollution in the Denver region.”
He added, “The way we reduce that pollution is by making our buildings all electric and more efficient.”
Heat pumps are far more energy-efficient than many traditional HVAC systems because they move heat rather than generate it. They can also provide both heating and cooling through a single system.
FROM OUR PARTNER
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Solar panels can save you more than $50k over their 25-year lifespan, and EnergySage can help you save as much as $10k on installation. Which begs the question — isn’t that worth an email or two?
That can translate into lower utility bills over time, especially when paired with tax credits and rebates like this one, and shoppers can compare options through EnergySage’s Heat Pump Marketplace.
For people who do not need a whole-home setup, Merino offers single-room, ultra-efficient HVAC systems at a lower price point. The company’s targeted heating and cooling option can be installed in under an hour.
The new rebate comes after Colorado’s federal HEAR program stopped taking Front Range applicants in April.
The program had provided qualifying households with up to $8,000 toward heat pumps and another $4,000 for panel upgrades.
Even so, some experts say demand for heat pumps is still growing.
Neil Kolwey, Southwest Energy Efficiency Project’s industrial program director, said, “With the HEAR rebates gone, the Power Ahead Colorado is more relevant, more important.”
He added, “Still, the loss of the HEAR rebates shouldn’t have a big effect; there is a lot of momentum in the market and still a reasonable amount of money in rebates.”
In addition to the rebate, Power Ahead Colorado is providing free energy advisers by phone or email to help residents and businesses sort through electrification questions, heat pump options, and expected savings.
Spotts said, “We really want to be a resource to guide people through this major investment.”
The $1,500 discount can be used alongside other available incentives.
Xcel Energy customers may also qualify for heat pump rebates of $300 to $700 per heating ton, and a 2026 bonus can raise that amount to as much as $2,250 per ton.
Emmett Romine, a vice president at Xcel Energy, said, “We’re excited to see Power Ahead Colorado launch a heat pump rebate that, when combined with Xcel Energy rebates and others, helps make upgrading to a heat pump more affordable.”
Power Ahead Colorado is developing an additional $50 million program that would offer 1,600 low-income households free heat pumps along with home energy upgrades.
And for homeowners looking to cut bills even further, EnergySage offers a free solar quotes comparison tool; with EnergySage’s help, the average person can get nearly $10,000 in incentives for solar purchases and installations.
Anyone considering electrification can also use EnergySage’s Heat Pump Marketplace to compare heat pump options.
“The way we reduce that pollution is by making our buildings all electric and more efficient,” Spotts said.
Kolwey said, “There is a lot of momentum in the market and still a reasonable amount of money in rebates.”
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Waaree Renewable Technologies has secured two significant engineering, procurement, and construction contracts. These contracts are for ground-mounted solar photovoltaic projects totaling 800 MWac capacity. The company received two Letters of Award for these substantial solar power plants. These projects are scheduled for completion during the financial year 2027-28. The contracts were awarded by a leading Indian renewable energy company.

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A broken or inefficient system can leave households with fewer safe ways to stay comfortable.
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As a punishing heat wave keeps more Connecticut residents inside, HVAC systems across the state are being pushed to their limits.
That strain is showing up in a spike in emergency service calls, and technicians have a simple warning for homeowners: Setting thermostats too low can overwork air conditioners during stretches of extreme heat.
Extreme heat and poor air quality are sending air-conditioning demand even higher, Fox61 reported.
Air conditioners pushed to unusually low thermostat targets during stretches of persistent heat can run nearly nonstop and still fail to hit the requested temperature, which helps explain why older or overworked systems begin breaking down as hot weather drags on.
For homeowners considering an upgrade, heat pumps are one option. They provide both heating and cooling and are significantly more energy-efficient than traditional HVAC systems, which can translate into lower monthly bills over time. They may also qualify for tax credits and rebates, making upgrades more affordable upfront, and EnergySage’s Heat Pump Marketplace can help people compare options.
For a smaller-scale alternative, Merino offers single-room, ultra-efficient HVAC systems at a lower price point. The company’s targeted heating and cooling setup can be installed in under an hour, which may appeal to people who need faster relief in one part of the home.
When cooling systems give out during a heat wave, the effects can go well beyond simple discomfort. Excessive indoor heat can pose serious health risks, particularly for older adults, young children, and people with medical conditions that make it harder to tolerate high temperatures.
Forcing an AC unit to work harder by setting the thermostat too low can drive up electricity bills while increasing wear and tear, raising the chances of a costly breakdown at the very moment repair crews are already overwhelmed.
FROM OUR PARTNER
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Solar panels can save you more than $50k over their 25-year lifespan, and EnergySage can help you save as much as $10k on installation. Which begs the question — isn’t that worth an email or two?
On smoky or polluted days, many people keep their windows closed and depend even more heavily on indoor cooling, meaning a broken or inefficient system can leave households with fewer safe ways to stay comfortable.
For immediate relief, experts often suggest starting with a few simple steps: keep blinds closed during the strongest sun, use fans to move air around, seal leaks that let cooled air escape, and avoid setting the thermostat to an unnecessarily low temperature.
Longer term, homeowners who want to slash their energy bills with solar can also use EnergySage, which offers a free solar quotes comparison tool. With EnergySage’s help, the average person can get nearly $10,000 in incentives for solar purchases and installations.
And for households considering a more efficient way to heat and cool their homes year-round, it may help to revisit EnergySage’s Heat Pump Marketplace to compare systems, pricing, and potential savings.
Extreme weather can leave already strained cooling systems with little room for error, especially when repair crews are trying to keep up with a surge in calls.
Get TCD’s free newsletters for easy tips, smart advice, and a chance to earn $5,000 toward home upgrades. To see more stories like this one, change your Google preferences here.
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Updated: July 26, 2026 @ 1:24 pm

The guide explains 2026 solar benchmarks, financing fees, tax-credit changes, quote red flags, and a five-step pricing check, plus a free calculator and independent review.
 Solar Quote Checker, an independent review service for U.S. residential solar quotes, today announced the launch of its AI-assisted quote review platform, giving homeowners a way to verify a solar proposal’s pricing, production claims, and financing terms before signing a contract. The launch responds to a first in the modern solar market: 2026 is the first year in two decades that homeowners are purchasing solar with no federal residential tax credit.
The Section 25D Residential Clean Energy Credit was terminated for expenditures after December 31, 2025 under the One Big Beautiful Bill Act (P.L. 119-21); per IRS guidance, a purchased system completed on or after January 1, 2026 receives no federal residential credit. Meanwhile, the U.S. Energy Information Administration’s July 2026 Short-Term Energy Outlook projects residential electricity at about 18.2 cents per kilowatt-hour in 2026, up nearly 5 percent from 2025. System pricing itself varies widely: the EnergySage marketplace reports a national average of $2.60 per watt, while Berkeley Lab’s 2025 data update, which covers 2024 installations, puts the median host-owned residential system at $3.50 per watt cash and $4.70 per watt when loan-financed. Against the cash median alone, that is a spread of $0.90 per watt — about $8,100 on a 9-kilowatt system.
“Homeowners are being asked to make a decision that published benchmarks put in the tens of thousands of dollars, from a proposal that may not even state the price per watt,” said Richard Mill, founder of Solar Quote Checker. “With the federal residential credit gone, there is no cushion for overpaying. We built an independent solar quote review so any homeowner can get a numbers-first read on their quote — what lines up with published benchmarks and what deserves a direct question to the installer — before signing anything.”
The service works from the homeowner’s own paperwork. Customers upload their solar proposal as a PDF or photo; AI extracts the details from the document, and every number in the report — price per watt versus published benchmarks, system size reasonableness, production estimates, and savings math — is computed by deterministic software, never by the AI. Reports frame findings as transparency questions to ask the installer, covering equipment models, warranty coverage, production assumptions, missing contract terms, and financing structures, including dealer fees, which a CFPB Issue Spotlight found typically run 10 to 30 percent of a system’s cash price and are usually financed into the loan rather than listed as a separate line item.
A Basic review is $149; a Pro review is $249 and adds equipment, warranty, financing, and incentives analysis plus negotiation scripts. Every review starts with a free preview, purchases are one-time with no subscription, and no account is required. Alongside the paid service, the company released a set of free tools: a solar cost calculator that requires no email address, a state-by-state cost guide, a quote comparison worksheet, and a full sample report.
“If you have a quote in hand, start with three numbers: total price, system size in watts, and the year-one production estimate,” added Richard Mill “Our job is to make that homework take minutes instead of weekends.”
The service is available now to homeowners nationwide at www.solarquotecheckup.com and works with quotes from any installer.
About Solar Quote Checker
Solar Quote Checker is an independent solar quote review service for U.S. homeowners. It is not an installer or a marketplace, receives no installer commissions, and sells no leads; revenue comes solely from report purchases. Its reports are factual and transparency-focused, designed to help homeowners ask better questions before they sign.
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Solar Quote Checker Publishes 2026 Guide to Fair Solar Panel Pricing
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Bangor Daily News
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Ten rooftop solar panels were one of the selling points of the Falmouth retirement community Fritz Farquhar and his wife chose to move into nine years ago.
The energy the solar panels generate in their independent living cottage at the OceanView help offset their electrical bill significantly, to the point where Farquhar only pays service charges in the summer, he said.
“I have apologized to my kids and grandkids for what my generation did to the environment,” Farquhar said. “I feel bad things that have happened on my watch and I’m glad to be in a place that’s tackling those issues.”
To meet a growing demand for efficient homes, OceanView, which offers both independent and assisted living as well as memory care services, is building nearly 40 new two-bedroom, two-bathroom units for older adults.
The project will add 34 all-electric, solar-powered apartments and three cottages to the community, all of which will be for people who still live independently, according to Mitchell DeBortoli, sustainability and operations coordinator at OceanView.
Construction began in April 2025 and is projected to finish early next year, but residents are already moving in as the homes become available, DeBortoli said.
The new homes fill a need for more housing for aging Mainers, as they allow them to downsize from larger houses that younger families need. But they also reveal a growing desire for more energy-efficient features on homes, either for cost-savings or environmental reasons, that developers and real estate agents are seeing throughout the state.
These new units aren’t the first effort by OceanView at Falmouth to add environmentally friendly features, DeBortoli said. The community offers 10 electric vehicle charging stations, has installed more than 120 heat pumps and has invested in off-site solar farms and rooftop panels.
“Focusing on the environment and integrating that into the lifestyle here is a big part of who we are,” DeBortoli said.
The desire for renewable energy systems in homes isn’t limited to southern Maine retirement communities.
Benjamin Hooper, owner of Little River Homes who’s developing dozens of homes in Belfast, said each house he builds has heat pumps, upgraded insulation and a “solar ready” roof. This means a home’s roof is built to withstand the weight of panels and the electrical wiring needed to operate them is already in place, so buyers can have the panels added for an additional fee.
The homes have been popular with many buyers so far, typically because they mean homes will have lower energy bills over time, Hooper said.
The average price of heating oil statewide sat at $4.42 per gallon as of last week, according to the Maine Department of Energy Resources. That’s a significant jump from five years ago when it cost roughly $2.60.
Meanwhile, more Mainers are choosing to warm their homes using propane and electricity and moving away from heating oil, data released earlier this year by the U.S. Census Bureau showed. Heating oil remains the most popular option statewide.
The number of Mainers heating their homes using electricity, like installing heat pumps, ticked up by more than 4% in 2020 to 2024 compared with the previous period, according to Census data.
“People are uncertain about the price of energy,” Hooper said. “If you have a house that generates its own energy, it doesn’t matter what prices do.”
Julie Williams, owner and CEO of Bangor-based ERA Dawson Bradford Co., said Maine homebuyers have sought properties with energy-efficient features like solar panels and geothermal heating systems for decades.
Even before demand for Maine homes hit a fever pitch during the pandemic, Williams said buyers looked for homes with electric vehicle chargers.
“If that feature wasn’t available on the property, buyers would have contractors install the chargers after the purchase,” Williams said.
Kathleen O'Brien is a reporter covering the Bangor area. Born and raised in Portland, she joined the Bangor Daily News in 2022 after working as a Bath-area reporter at The Times Record. She graduated from… More by Kathleen O'Brien
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Power outages can cause major problems when unprepared, and battery storage makes a home so prepared that you typically don’t even notice when the power goes out.
Photo Credit: iStock
Even a brief loss of power can upend life at home. In one case, a homesteader said a fairly short blackout changed battery storage from something that seemed optional into something that felt essential.
In a post on r/homestead on Reddit, the original poster asked: “Has anyone else had one outage that completely changed how you looked at battery storage?” 
“Ours wasn’t even that long,” they continued, “but it was enough to remind me how many daily routines depend on electricity, from keeping food cold to running the well, keeping the freezer going, and staying connected.”
One commenter described a four-hour outage as “borderline catastrophic” and added, “I thought about what it would be like if we had an outage that went 4 days instead of just 4 hours, and I was like ‘nope, not happening.'”
For homeowners weighing those same questions, it may be worth exploring EnergySage’s free tools to get information about home battery storage options, including competitive installation estimates. Pila is also worth looking into, as its plug-and-play batteries are priced at a fraction of what a whole-home backup system would cost. 
For rural households, losing power can affect far more than lighting. It can shut down a well pump, put freezer-stored food at risk, cut off internet access, and disrupt the routines people rely on for work, school, and basic safety.
Adding battery storage is one of the best ways to protect your home during outages, save money on energy, and go off-grid. When paired with solar, a battery can help households use more of their own electricity after sunset or during expensive peak-rate hours, lowering utility bills while reducing reliance on fuel deliveries.
Generators remain a common backup option, as several commenters noted, but they come with tradeoffs, including fuel storage, maintenance, and noise. Batteries can provide quieter, lower-maintenance backup power, particularly for essentials such as refrigeration, communication, and water systems.
FROM OUR PARTNER
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Solar panels can save you more than $50k over their 25-year lifespan, and EnergySage can help you save as much as $10k on installation. Which begs the question — isn’t that worth an email or two?
What actually needs to stay on varies from one household to another. For some households, that may mean the refrigerator, router, a few lights, and charging devices. For others, especially on rural properties, it could also include a well pump, boiler, or outbuildings.
That difference can help determine whether a smaller battery setup makes sense or whether a whole-home system is worth the investment. If you already have solar, battery storage can make that system far more useful during outages. If you do not, batteries can still offer backup protection and utility bill savings, depending on how your electricity provider structures rates.
Others in the discussion favored having multiple layers of backup. One commenter explained, “We have a backup propane generator if the main solar system fails,” and also described keeping separate systems for the well and other critical functions. That kind of planning will not prevent outages, but it can make them much less disruptive.
As the OP put it, “The numbers are easy to compare. It’s the peace of mind and the little day to day things that don’t show up on a spec sheet that seem to matter most.” 
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When entering a dark room, most people turn on a light without thinking twice, but for 730 million people worldwide, access to electricity is scarce.
“People deserve to have these basic necessities. It shouldn’t be something they have to fight for,” said Jacob Hoffman, a rising junior mechanical engineering major at UMass Lowell.
Hoffman and seven other UMass Lowell students completed a mechanical engineering directed study during the spring semester, which included a one-week trip to Sosúa, Dominican Republic, where they installed solar panels for residents without reliable electricity. The trip was sponsored by UMass Lowell’s Center for Energy Innovation (CEI) and the Energy Access Foundation, whose mission is to accelerate access to sustainable energy in developing countries.
“We were able to help people in need and, at the same time, help educate students and give them a valuable experiential learning experience,” said Christopher Niezrecki, CEI’s director and a professor in the Department of Mechanical and Industrial Engineering, who led the directed study. “The students got a better understanding of what life is like without the resources that we take for granted.”
Working alongside representatives of the Energy Access Foundation, the students installed solar panels at a school in the Bella Vista neighborhood of Sosúa. They then presented to the school about solar power and how it will help their educational experience.
“We were all very touched by how much this means for the community and how this will help them keep the school up and running when they have power outages,” said Nuala Rodgers, a rising junior climate change and sustainability major.
The students also installed solar power systems at three homes in the area. They designed the systems before the trip, after researching quality, cost-effective solar components and studying how much sun Sosúa receives.
“It’s really nice that the projects we engineered and installed are making an impact on people’s lives by bringing them light and energy,” said Ryan Aitsaid, a rising junior electrical engineering major.
The students learned a valuable lesson during the trip: Not everything goes as planned. They faced multiple challenges, including tin roofs being too sharp to wrap wires around and screw holes from mounting brackets posing leak hazards. However, they got creative and found solutions for each issue that appeared.
“Designing a system in the classroom, everything is working under ideal conditions, but when we actually got down there, we had to have backup plans to adapt our system,” said Jackson DeMello, a rising junior electrical engineering major. “You need to have that hands-on experience to actually understand how something will work.”
While in the Dominican Republic, the students immersed themselves in the culture. They tried local cuisine, participated in activities like surfing and explored the nearby city of Puerto Plata.
“I felt connected to the people,” said Nadia Eshun, a rising junior political science major. “They really made me feel at home.”
The students are getting updates from the Energy Access Foundation on how their solar power systems are performing.
“An important part of installing these projects, both at a larger scale at the school and smaller ones at the homes, is that our mission wasn’t just to install and forget about it. It’s about sustained community impact,” Rodgers said. “In the case of the school, we heard that they had an all-day power outage recently, but with the power generated from our solar panels, they were able to keep school in session all day.”
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LOCAL COLUMN
Over 50,000 New Mexico homeowners and businesses have taken advantage of our state’s abundant sunshine by installing solar on their property to lower their energy bills. These customers are contributing to the electric grid’s efficiency and reliability, helping to reduce the need for new energy infrastructure and improving the environment.
The current Public Service Company of New Mexico net metering program, the mechanism that determines the compensation that solar owners receive for the electricity they produce, has played an important role in incentivizing customer-sited solar.
Now New Mexico’s largest utility wants to change its net metering program and its commitment to customers with solar installations and to customers who install solar in the future. PNM recently convened a meeting with stakeholders to announce several changes it wants to make for customers who have solar, including eliminating customers’ ability to bank credits for the excess production generated by their solar systems and forcing these customers into other rate arrangements.
Unfortunately, these kinds of actions are pursued by electric utilities to slow the growth of customer-sited solar and downplay its widespread benefits. In response to these actions, industry experts have undertaken the process of quantifying the value of solar, both to individual residents and businesses and the overall electric system. This undertaking is widely referred to as the “value of solar” (VOS).
Many studies show that compensating solar generation with traditional net metering undervalues rooftop solar once societal and environmental benefits (like avoided carbon emissions) are factored in. A 2025 Lawrence Livermore study on VOS evaluated the comprehensive economic, environmental and societal benefits of solar energy, finding it benefits homeowners and businesses by lowering costs. It also provides public health benefits and climate damage reduction.
The solar industry has always valued its relationship with electric utilities as they play a vital role in helping grow customer-sited solar, while maintaining a safe and reliable grid. But recent actions by PNM threaten this relationship. Not only has PNM not offered an alternative to reducing the compensation in its net metering program, but the company has opposed customer-sited solar initiatives like meter collar adapters, energy storage and virtual power plant legislation.
The New Mexico solar industry plays an important role in helping to reduce the energy burden for New Mexicans and contributing to our state’s economy. There are currently more than 70 solar companies in New Mexico, which include solar manufacturers, installers and developers, employing more than 2,000 New Mexicans. This does not account for businesses that provide products and services for the industry such as electrical supply houses, professional service companies and lending institutions.
It is time for PNM to embrace the changes going on around the world in the energy sector and work with customers and the local solar industry to continue the progress we have made for a strong, clean energy future that benefits everyone. The solar industry looks forward to working with PNM and other New Mexico utilities to best serve the energy needs of our state.
Jim DesJardins is the executive director of the Renewable Energy Industries Association of New Mexico.
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First Solar Stock Faces New Pressure From China BRI Green Energy Spending  simplywall.st
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The Ministry of the Interior’s Standards for Installing Photovoltaic Power Systems on Buildings is to take effect on Aug. 1, with officials estimating that it would add about 660 megawatts of new capacity annually.
The measure is expected to increase rooftop solar development, with companies including Sino-American Silicon Products Inc (中美晶), United Renewable Energy Co (聯合再生), Motech Industries Inc (茂迪), Gintech Energy Corp (元晶), Alpha Solar Corp (安集) and Gintung Energy Corp (聚恆) preparing for related projects.
The Sustainable Solar Development Association said that the policy could help meet growing demand for electricity from the semiconductor and artificial intelligence (AI) sectors and improve urban resilience.
Photo courtesy of the Pingtung County Government
Under the new regulations, newly constructed, expanded or renovated buildings with a floor area of more than 1,000 square meters would be required to install solar photovoltaic systems.
Sino-American Silicon Products said its Yilan-based subsidiary Ever Energy is developing low-orbit satellite-related applications while continuing to develop its rooftop solar business. It added that its fire-resistant modules are in mass production.
Alpha Solar chairman Huang Kuo-tung (黃國棟) said the company focuses on rooftop solar products using building-integrated photovoltaic materials.
The modules are designed to function as building materials and are fire-resistant, waterproof and walkable, he said.
The company said the products are in small-scale shipments and could be installed on rooftops and exterior walls.
Motech Industries produces building-integrated photovoltaic products, including tile-type modules, floor and wall panels and colored glass modules.
Gintung Energy chairman Chou Heng-hao (周恒豪) said the company obtained TUV Rheinland operations and maintenance certification in Germany 10 years ago, the first EPC firm in Taiwan to do so.
The company operates a 24-hour monitoring system for solar plants and provides maintenance, diagnostics and upgrade services for underperforming facilities, he said.
Rooftop solar installation on buildings is already a common regulatory requirement in many countries, the Solar Energy Sustainable Development Association said.
It added that the electricity generated could help meet local demand and support industries with high power consumption, including semiconductor and AI supply chains, while also helping companies comply with carbon reduction requirements under initiatives such as RE100 and the EU’s Carbon Border Adjustment Mechanism.
BRACE FOR WET DAYS: Heavy rain is expected in Hualien, Taitung and the Hengchun Peninsula, and northern areas would see localized showers, it said A tropical depression east of the Philippines is expected to strengthen into Typhoon Noul, and a sea warning was likely to be issued as early as 11:30pm last night, the Central Weather Administration (CWA) said yesterday. The storm would be closest to Taiwan tomorrow, and bring heavy rain to Hualien and Taitung counties, as well as the Hengchun Peninsula (恆春半島), it said. If the storm strengthens into a typhoon, it would be the 12th this year. As the storm has maintained a steady speed, it is expected to cross the Bashi Channel relatively quickly, CWA forecaster Tseng Chao-cheng (曾昭誠) said. The storm is forecast
The Chinese Nationalist Party (KMT) caucus yesterday submitted a motion to suspend the Cabinet’s NT$44.2 billion (US$1.37 billion) drone industry plan, calling the program “bloat” and questioning its legality. The comprehensive uncrewed vehicle industry development plan seeks to support the growth of a self-sufficient domestic drone industry for national defense through 2030. The KMT caucus introduced the motion to suspend the plan as part of interparty negotiations over this year’s budget, which remains stalled in the legislature. Local Chinese-language media reported that the KMT caucus wrote in the motion that the Executive Yuan’s belated approval of the plan in October
Taiwanese planning trips to Morocco should carefully assess their travel plans, the Ministry of Foreign Affairs (MOFA) said today, following reports that a travel agency had suspended its visa authorization letter application service for people visiting the country. Lion Travel yesterday said in a notice on its Web site that the Moroccan embassy in Japan had stopped accepting visa applications, prompting the company to suspend its visa authorization letter application service for people visiting Morocco. Taiwanese are required to obtain an entry visa authorization letter before traveling to Morocco, the Bureau of Consular Affairs Web site says. Applications are submitted through the Moroccan
A tropical depression that is expected to develop into a tropical storm could trigger a sea warning for Taiwan as early as tonight, the Central Weather Administration (CWA) said. The system is forecast to become a tropical storm late tonight or early tomorrow, at which point it would be named Noul, the 12th named storm of this year’s Pacific typhoon season, the weather agency said. As of 8am today, the tropical depression was centered about 1,110km southeast of Oluanpi (鵝鑾鼻), Taiwan’s southernmost point, moving west-northwest at 29kph, with maximum sustained winds of 54kph, CWA data showed. The system is expected to affect Taiwan

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Home – Tech – Switzerland hid the world’s first solar plant between railway tracks: one year on, neighbors want in
Putting photovoltaic panels directly beneath an active train sounds like the kind of clean-energy idea that might fail the first time winter, vibration, or a maintenance crew arrives.
In Buttes, Switzerland, the world’s first removable solar power plant on a working rail line has now been crossed by more than 11,000 trains without a reported safety incident or disruption to daily service.
The early result is simple but important, since a railway may be able to carry passengers above and generate electricity below.
The plant is tiny by utility standards, covering just 328 ft. of track and producing more than 16,000 kilowatt-hours since May 2025. Still, its real value is not the power from one village line, it is the possibility of using existing rail corridors for solar generation without occupying additional land.
Sun-Ways installed the Buttes system on April 24, 2025, placing 48 panels rated at 380 watts between the rails and on top of the railroad ties. Together, they provide 18 kilowatts of peak capacity and send their electricity into the local grid. The three-year pilot is scheduled to continue through April 2028.
The important part is not the panel itself. It is the removable mounting system, which lets rail crews reach the track when they need to replace a tie, inspect equipment, or carry out welding. Sun-Ways founder Joseph Scuderi told Swissinfo, “We have achieved our objectives, both in terms of railway safety and electricity production.”
Trains on the line can travel at up to about 56 mph, and the installation has remained in place through more than 11,000 passages. TransN, the public operator responsible for the route, reported no operational conflict and said, “There have been no conflicts with infrastructure, maintenance, or train traffic.”
It also received no reports that reflected light had dazzled train drivers.
That absence of drama matters in rail engineering. Solar output can be measured quickly, but safe coexistence with inspections, snow, vibration, and tightly timed maintenance takes longer to establish. That is why SNCF is using the Swiss pilot to study panel installation and removal, glare, track inspection, dirt buildup, output, and maintenance impacts through 2028.
Since May 20, 2025, the pilot has generated more than 16,000 kilowatt-hours, despite being shut down for roughly a month because of snow and planned technical work. That is about the yearly electricity use of three or four Swiss households. Small? Yes, but the first job of this project was to show that solar equipment could share a working railway safely.
Sun-Ways estimates that suitable sections across Switzerland’s 3,306-mile rail network could produce as much as 1 billion kilowatt-hours per year. By the company’s calculation, that would cover the annual use of about 300,000 homes, or close to 2% of the country’s electricity consumption.
Those are projections, not measured national results, and they depend on sunlight, tunnels, maintenance schedules, grid access, and cost.
The quiet advantage is land. The strip between active rails cannot also serve as a farm or building site, so using it for solar could add generation without claiming a second parcel. In practical terms, that may avoid some of the siting disputes that can slow renewable projects.
Installation is also designed to be mechanized. A special machine developed with Swiss rail contractor Scheuchzer can reportedly lay up to 984 ft. of panels per hour, while a three-panel module measuring about 20 ft. can be disconnected and removed in roughly 10 minutes.
Scuderi also said airflow from passing trains has prevented troublesome dust buildup, reducing the need for the brush system first considered.
Generating electricity in a straight line for miles creates a different problem from placing panels on one roof or in one compact solar farm.
Julien Pouget of the University of Applied Sciences of Valais has warned that present equipment is not well suited to stretches longer than about 1,640 ft. because the power must be collected, raised to a useful voltage, and moved efficiently over distance. That is where a clever rail idea turns into a serious grid-engineering challenge.
A study published in July 2026 offers one possible route forward. Researchers modeled a 3.1-mile railway-based solar plant connected through a medium-voltage direct-current network, with 3.4 megawatts of peak power and 40 converter stations rated at 100 kilowatts each.
Their model kept medium-voltage variation within 3%, although modeling a network is not the same as proving its cost, durability, and safety in daily rail service.
France has taken the most formal step so far. SNCF, which describes itself as France’s largest electricity consumer, signed a collaboration agreement with Sun-Ways in November 2025 and announced it in February 2026. The deal gives its teams access to operating data and technical feedback while they examine whether the system could fit France’s roughly 17,400 miles of rail.
Italy is at an earlier stage, but the conversation has moved forward. Sun-Ways has signed a collaboration contract with an unnamed Italian business partner that is in contact with Rete Ferroviaria Italiana, although a site and launch date have not been announced.
South Korea has approved a two-year test near Osong station, while an Indonesian engineering company says more field testing is needed.
Sun-Ways wants Switzerland’s Federal Office of Transport to grant final approval before the scheduled end of the pilot. The regulator set a three-year test, which means a final decision may still depend on more seasonal data. That caution is understandable when power equipment is sitting inches below moving trains.
At the end of the day, the breakthrough is not that 48 panels supplied the equivalent annual power use of a handful of homes, but that an overlooked strip of infrastructure appears capable of producing electricity without giving up more land or interrupting the service already using it. The next test is whether the economics and electrical architecture can scale as neatly as the idea. 
The study was published in Energies.

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Germany and Switzerland team boosts all-perovskite solar cell to 27.3%, targets 30%  Yahoo Tech
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“What went through my mind was, oh, there must be some sort of issue.”
Photo Credit: iStock
Summer air-conditioning demand is pushing power costs higher in Tampa, Florida and some residents say their monthly electric bills have risen to double or even triple their usual amount.
A hurricane-recovery fee is scheduled to come off customer bills soon, but with intense heat still gripping the area, many households expect expensive weeks to continue.
Residents around Tampa told FOX 13 that their latest power bills are far above normal. Among them is Tampa resident Jade Scott, who claimed her bill climbed from about $200 last month to roughly $470.
“What went through my mind was, oh, there must be some sort of issue,” Scott told the station. “Like, you know how the water bill’s high when there’s a leak? I thought, surely there must just be something going on. But no, that’s just the bill!”
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Another Tampa resident, Ignacio Torano, told FOX 13 that this month’s electric bill reached $800, compared with the roughly $300 to $400 he usually pays.
Tampa Electric (TECO) says customers should get some near-term relief beginning August 1, when a surcharge connected to 2024 hurricane recovery is removed from bills. TECO spokesperson Cherie Jacobs said residential charges are expected to decrease by 11-12%.
“Just want to remind folks, it is still summer,” Jacobs said. “Your energy use is still going to be higher than it is, say, in December.”
For many households, an 11-12% drop will help, but it may not fully offset heavy summer air-conditioning use.
FROM OUR PARTNER
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers that can help you save as much as $10k on installation.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best local options for your needs, and their expert advisers can help you compare quotes and pick a winner.
High utility bills can quickly force people into difficult tradeoffs, especially during stretches of dangerous heat. Residents told FOX 13 that they have pushed the thermostat up to at least 78 degrees in an effort to lower costs, and Scott said the warmer indoor temperatures have made it harder to sleep.
For qualifying customers, TECO says it has flexible payment plans available, as well as online and in-home energy audits.
The company also provides rebates to homeowners who replace older air-conditioning equipment with more efficient systems, and customers in its Prime Time Plus program can receive monthly bill credits.
Jacobs also urged struggling customers to reach out directly.
💡Go deep on the latest news and trends shaping the residential solar landscape
“Right now, we know that prices are going up for everyone from the gas pump to the grocery store, and we feel that,” she said, adding that the utility can help connect some customers with extended payment arrangements and financial assistance.
Some Tampa residents remain unconvinced that relief will come anytime soon.
“I don’t have any words for it,” Torano said. “I was shocked. I was like, alright, this isn’t right. Something’s wrong.”
“And my sleep has gotten worse, and you know what? I don’t believe the bill’s going to be less,” Scott added.
Going solar is one of the best ways to save money on home energy over time, and EnergySage‘s free services to get quick solar installation estimates and compare quotes can make the process easier. With EnergySage’s help, the average person can save up to $10,000 on solar purchases and installations. Meanwhile, EnergySage’s solar map shows the average cost of a home solar panel system on a state-by-state level, as well as details on solar panel incentives for each state. 
Adding battery storage to a solar setup is a great way to further protect your home during outages, save money on energy, and go off-grid. Batteries can also help households keep essential appliances running when the grid goes down during major storms. Readers can explore EnergySage’s free tools for information about home battery storage options, including competitive installation estimates.
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By: Luis Reyes
Published: Jul 26, at 6:30am ET
Every solar farm you have driven past looks about the same from the road. Rows of dark panels tilted up at the sky, angled south, all of them waiting on noon.
On a gravel lake southwest of Munich there are 2,600 panels standing dead upright instead. Edge-on to the sun, facing east and west in long rows, like somebody fenced the water in glass.
That is not a mounting shortcut or an unfinished job. It is the whole design, and the reason for it starts with a German law about how much of a lake you are allowed to keep in the shade.
The site is a working gravel pit at Gilching, in Bavaria’s Starnberg district. The builder is SINN Power, an engineering company based a few miles up the road in Gauting. The array went live on the grid on August 21 last year and had its ribbon cut on October 10, with Bavaria’s Minister-President Markus Söder taken out on a boat to look at it.
Rated capacity is 1.87 megawatts, good for roughly 2 gigawatt-hours a year. That is a small plant, and the claim being made is not about size. SINN Power calls it the world’s first vertical floating solar plant, and Germany’s Federal Ministry for Economic Affairs and Energy has used the same wording in its own energy transition newsletter. pv magazine, which has followed the project since it was announced in April 2024, hedged differently and called it the largest floating plant with vertically mounted panels.
The lake belongs to Kies- und Quetschwerk Jais, the gravel and crushing business that dug the hole the water sits in. Its managing director, Gottfried Jais, is a co-investor in the plant, and most of the electricity never leaves the property.
That matters more than it sounds. A gravel works runs crushers, conveyors and pumps, and it runs them in daylight, on weekdays, roughly March through December. Jais told pv magazine the arrangement “does not require any land and adapts perfectly to our production plant.”
So the customer is standing on the shore. No transmission fight, no land lease, no farmer to talk round. The company that owns the water owns the power station and burns most of what it makes.
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The engineering problem here is a good one. Lay a panel flat on a float and the wind mostly slides over the top of it. Stand it up and you have built a sail on a lake, which is an efficient way to lose a solar farm in a spring storm.
SINN Power’s answer, patented as SKipp, was to stop resisting. Each float carries a keel-like structure hanging up to 5.2 feet (1.6 meters) below the surface, weighted with concrete at the bottom, and the modules are held upright against a cable system rather than a rigid steel frame.
When the wind loads up, the rows lean. pv magazine describes the mounting as built so the modules deflect under load through those cables, shedding wind resistance instead of absorbing it, while the submerged section holds the array steady as the lake level moves.
The rows also sit at least 13 feet (4 meters) apart in open water corridors. That gap does three jobs at once: it lets wind through, it stops one row shading the next, and it leaves the water underneath open to light and air. Austria took the opposite route with the same basic instinct, planting 800 vertical solar crosses on a glacier shaped so the wind scours snow away from them.
None of this arrived untested. SINN Power had been running a smaller vertical pilot at Baden-Baden for more than 18 months before Gilching went in the water.
Since January 1, 2023, German water law has permitted floating solar only on artificial or heavily modified water bodies, and it caps any installation at 15 percent of the surface with a minimum 40-meter setback from the bank. The rule lives in section 36 of the Federal Water Resources Act and was written on ecological grounds.
For a flat array that ceiling is brutal arithmetic. Panels lying down cover exactly the area they occupy, so 15 percent of the lake is 15 percent of the possible plant, and that is the end of the conversation.
Stand them up and the shadow gets thin. Gilching covers 4.65 percent of the water. Even with a second phase already drawn up, another 1.7 megawatts, SINN Power says both stages together will stay under 10 percent.
There is a live legal question sitting underneath all of it. Lawyers at the German firm ARQIS have argued that for vertical systems the 15 percent should be measured against the water actually covered rather than the full project footprint, and noted that no German authority or court has ever defined what “cover” means in this context. The Bundesrat asked the federal government to revisit the rule in a resolution passed in September 2025. It has not been changed.
The east-west trick is not only about squeezing past the water rule. It moves when the power arrives.
Panels aimed east and west make less at midday than a south-facing array and more in the morning and evening. In a country carrying roughly 117 gigawatts of solar, that is where the value went.
Germany logged 573 hours of negative day-ahead power prices in 2025, a record, according to Bundesnetzagentur figures, and they bunch up in sunny midday windows when everybody’s panels produce at the same moment. On May 1 this year the German day-ahead price hit the market floor of minus €499.99 per megawatt-hour.
Philipp Sinn, SINN Power’s founder and chief executive, introduced the plant at the opening as “a photovoltaic system that produces electricity when conventional power plants are standing still.” Turn that around and it says the same thing: it produces when everyone else’s solar has already flooded the market.
The federal economy ministry puts the commercially viable floating solar potential on Germany’s artificial lakes at 1.8 gigawatts with south-facing modules and 2.5 gigawatts with east-west ones. Same lakes, same 15 percent rule. Turning the panels sideways is worth about 700 megawatts on its own. Fraunhofer ISE and RWE count 6,043 artificial lakes of at least one hectare in Germany, and roughly 70 percent of them are gravel pits.
SINN Power’s October announcement said the gravel works had cut its grid electricity purchases by close to 60 percent in early operation, with up to 70 percent expected once things settled down. The write-up the company published in November said grid draw fell 70 percent across the first three weeks, a stretch in which the plant made more than 100 megawatt-hours in autumn weather.
Both are self-consumption figures, and it is worth being precise about that. Neither is a panel efficiency number, neither has been independently audited, and the 70 percent describes how much less power the gravel works buys, not how well the panels convert sunlight.
The ecological claims are the company’s as well. SINN Power reports waterfowl nesting on the floats, fish gathering around the concrete ballast where algae form on it, and monitoring buoys showing water quality holding or improving. Independent work on this question does exist elsewhere: a Dutch quarry lake study counted fish and invertebrates under a flat array and found more life than the critics predicted. Nobody outside the company has published on this particular lake.
The freshest thing about the plant is who has been standing on the bank looking at it.
During Intersolar Europe in Munich, June 23 to 25 this year, SINN Power skipped the usual booth routine and drove visitors 20 miles west to the water instead. The company says groups from India, Taiwan, China, Israel and Turkey came out to see it running.
That is the real test of a format record rather than a size record. A 1.87-megawatt plant does not move a national grid. A 1.87-megawatt plant that a Taiwanese delegation flies in to inspect might change what gets built on the next few thousand lakes.
American reservoirs are the version of this argument with the volume turned up. A federal NREL assessment published in February mapped floating solar potential on US federally regulated reservoirs large enough to power tens of millions of homes, on water that already has substations and transmission rights attached. The United States has no 15 percent coverage statute forcing anyone to stand panels on edge. Germany’s constraint is what produced the design.
The second Gilching phase is still in planning. The water rule is still on the books, still undefined for vertical hardware. SINN Power has meanwhile taken the same structural idea to sea, dropping a platform called SOcean into open water off Heraklion, Crete, on May 27 for a multi-month endurance run under a German federal research programme, carrying solar modules and wave energy hardware together.
Whether any of it scales depends on a sentence in German water law that nobody has yet defined. What is already settled is smaller and harder to argue with. The gravel company’s power bill went down, the lake is still a lake, and 2,600 panels have now stood through a Bavarian winter standing straight up.
Did we nail it or blow it?
Luis Reyes · Jul 3, 2026
Luis Reyes · Jul 24, 2026
Luis Reyes · Jun 27, 2026
Luis Reyes · Jul 13, 2026
Luis Reyes · Jul 12, 2026
Luis Reyes · Jul 8, 2026
Olivia Richman · Jul 25, 2026
Luis Reyes · Jul 25, 2026
Luis Reyes · Jul 25, 2026
Luis Reyes · Jul 25, 2026
Luis Reyes · Jul 25, 2026
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Solar panels have been named as the 'likely' cause of a recent fire that completely destroyed a mental health hospital ward.
The Springwood unit at Malton Hospital, located in North Yorkshire, England, was set ablaze on 8 July. Local fire and rescue services say the building was "fully engulfed" when crews arrived.
All 15 patients were safely evacuated from the unit, and no injuries were reported. A full report into the blaze is expected in the coming weeks.
The fire follows three similar incidents at primary schools in England over the last 12 months, which have all been blamed on solar panels. This led to around 80 schools in the county of Suffolk switching off their solar panels as a precautionary measure.
The council says the primary school fires were all believed to have had different causes rather than a common fault with the panels.
However, it still casts a light on the potential dangers of installing rooftop solar on public buildings and residential homes.
Solar photovoltaic (PV) panels, which are individual solar cells that convert sunlight into power, have become the most competitive source of electricity in many parts of the EU.
According to Eurostat data, 47.5 per cent of the EU's electricity consumption came from renewables in 2024, and solar PV accounted for almost a quarter of that (23.4 per cent).
The sheer boom in solar panels has increased the number of fire incidents and subsequent media attention around them. However, the per-system risk remains extremely low.
Data from Dutch research institute TNO reported that, between 2015 and 2018, only 27 fires on buildings with PV occurred in the Netherlands – with residential buildings accounting for 23 of the fires.
With 170,000 new PV systems installed on residential buildings in 2018, this represents less than 0.014 per cent of homes with solar panels.
Between 1993 and 2013, German research institute Fraunhofer ISE reported that 0.006 per cent of PV installations caused a fire with major damage. Of Germany's total 1.3 million solar PV installations in 2013, 350 were involved in fires, while 120 were attributed to the actual cause of the blaze.
But data on solar-related fires remains sparse.
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A study published last month in the journal Science Direct analysed national statistics from four European countries. It found that the number of incidents per 100,000 installations was found to be approximately 5-22 for the UK, Italy and Sweden, compared with 75 in Slovenia.
"These differences are likely influenced by variations in data collection methods and inclusion criteria across the countries," the report says.
Researchers found that about 68 per cent of fires remained confined to the PV equipment, while 24 per cent caused damage to adjacent surfaces – such as the roof. In five per cent of cases, significant fire spread occurred, while buildings being completely destroyed only accounted for three per cent of fires.
The European Commission says that solar panel fires occur at a rate "significantly lower" than that of many common household appliances.
"When issues do arise, they are typically linked to poor installation or faulty components, not the solar technology itself," the bloc adds.
Across Europe, strong safeguards are in place to keep solar installations safe. This includes making sure that all panels (whether they're imported or produced in the EU) meet strict technical requirements.
"To ensure systems are installed properly, EU countries must have certification schemes (or equivalents) for solar photovoltaic installers and designers, as required by the Renewables Directive," the Commission adds.
"These schemes include training programmes and public lists of certified installers."
Any system that captures the energy of the sun is likely to have some level of fire risk. This is particularly true for installations that are ageing and poorly maintained.
To lower the risk of solar panel fires, only use accredited installers and do not attempt to attach panels to rooftops yourself. Wall-mounted inverters should also be bolted onto non-flammable surfaces and away from heavy debris or storage materials.
Homeowners should also have regular inspections, carried out by professionals, to check and service panels every couple of years. It is also worth inspecting your panels after extreme weather, such as strong winds, to make sure nothing has shifted.
Lynch explained that Shanahan will essentially go through the same concussion protocol as players before being deemed fit to return.
The Philadelphia Phillies play the New York Yankees this Sunday, here's how to watch.
MLB trade deadline season is officially here.
The offer also reportedly would have exceeded Trent McDuffie's four-year, $124 million deal for the largest cornerback contract in NFL history.
Veteran utility man Kiké Hernández will also reportedly be activated on Tuesday.
Cease had a no-hitter going into the sixth inning and finished with 12 strikeouts, the final three in the ninth inning.
Jones would provide a 3-point threat to an OKC team that traded three of its best shooters this offseason.
The 36-year-old catcher now has 318 career home runs, all with Kansas City.
Are you ready to dive into Shark Week 2026?
Kuss was in position to be the first American until two crashes cost him the lead.

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For decades, pundits and politicos have been telling us that solar energy is right on the cusp of global dominance. For instance, way back in 1978, the year I graduated from high school, Ralph Nader claimed that “everything will be solar in 30 years.”
In 1979, President Jimmy Carter called for “a national commitment to solar energy” so that the US would “derive 20% of all the energy we use from the sun” by 2000. Twelve years later, three officials at the Worldwatch Institute claimed that “within a few decades” the US “might get 30% of its electricity from sunshine.”
In 1996, Senator Ted Kennedy said that “solar energy may well become the primary energy source for America and the world in the 21^st century.”
In 2021, President Joe Biden released a plan claiming that the US should obtain nearly half of its electricity from solar by 2050. Three years after that, the Energy Institute, which publishes the Statistical Review of World Energy, enthused that “China is establishing itself as a green energy leader with an unrivaled build-out of renewable energy and export of renewable technology.”
In April, Ember, the London-based consultancy, gushed that “Solar surge halts fossil electricity growth worldwide in 2025.” The article went on to say that solar met almost three-quarters of the growth in global electricity demand and that the increase in solar “drove clean power to meet all global electricity demand growth in 2025.”
Let me be clear: Solar is growing. It’s growing on rooftops and in utility-scale projects. I have 8.5 kilowatts of solar panels on the roof of my house in Austin. (I got three different subsidies to do the installation.) Over the past decade, global solar output has been growing by an average of 27% per year. That means solar is doubling every three years or so. That growth continued last year, when, according to the Statistical Review of World Energy, global solar production jumped by 30%.
Those are gobsmacking numbers. But amid the never-ending hype, it’s time for a reality check. And here it is: The deployment and use of solar energy is dominated by a handful of wealthy countries while the locations that desperately need more low-cost juice — and African countries in particular — are largely left in the dark.
I’ve spent several days crunching the latest numbers to give you the facts, not the spin, about solar energy. Here’s a sober look at solar with 11 charts.
This Substack is reader-supported. To post comments, receive all new posts, and access the archive, please consider becoming a paid subscriber.
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The agreement covers 5 GW of manufacturing capacity and will combine Caelux's energy-producing solar glass with Rayzon Solar's TOPCon solar modules, enabling the production of hybrid perovskite-silicon modules with efficiencies of up to 28 percent.
July 23, 2026. By Abha Rustagi

Renewables Will Become the Backbone of India’s Grid, Says Wärtsilä’s Archana Bhatnagar

NoPo' Gadhadar Reddy Explains India's Deep-Tech Opportunity in Single-Walled Carbon Nanotubes

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Hybrid Projects Need Smarter Execution, Explains Shantanu Upasani, Head of Construction, ENGIE

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India wants to build its own solar industry from the ground up. But it still can’t cut the cord from Chinese suppliers. That contradiction is now shutting down factories across the country.
Reuters reports that a shortage of domestically made solar cells has forced several panel manufacturers to halt operations entirely. Companies now face waits of up to eight months for local cells that were meant to replace Chinese imports.
Industry insiders warn this bottleneck threatens to derail India’s 2030 solar power targets.
India wants solar independence. Chinese parts still call the shots.
 A Third Of Factories Down
The numbers tell a stark story. Nearly a third of India’s 140 small and medium-sized solar panel manufacturers have already stopped production, according to India’s Solar Manufacturers Association.
Those idled plants account for 60% of the country’s national manufacturing capacity. The remaining factories aren’t faring much better. They’ve scaled back to running just once every three to four days.
Going local comes with a steep price tag right now. Manufacturers without their own cell production lines face six to eight month waits for domestic cells. That delay has pushed the cost of India-made panels to nearly double the price of panels built with Chinese cells.
Shailendra Shukla, chairman of manufacturer Icon Solar, put it bluntly. “We have suffered a lot due to the domestic cell unavailability for the past three months,” he said. He expects his company’s output to plunge from 3.2 gigawatts to roughly 1 gigawatt.
Mandate Meets Reality
Earlier this year, the Indian government started to force the issue. From June 1 onward, solar projects must use compliant domestically produced cells to cut reliance on imports. The policy backfired in practice. India’s local cell production capacity simply couldn’t keep up, leaving companies with plenty of module capacity but no cells to put in them.
The scale of the mismatch is striking. Indian firms have built roughly 200 gigawatts of solar panel manufacturing capacity. Solar cell capacity, by contrast, stands at just about 27 gigawatts, with actual operational output running even lower at 16 to 18 gigawatts.
Closing that gap won’t happen overnight. Rajan Kalsotra, senior consultant at German research firm EUPD Research, estimates it could take India three to five years to build enough domestic cell capacity to match its module output.
Solar power reaches even India’s temporary settlements.
A Supply Chain Still Made In China
India’s photovoltaic industry has leaned on China’s supply chain for years, and that dependence hasn’t loosened. About 95% of India’s solar cell imports come from China. Imports in the last fiscal year rose 37% year-on-year to roughly 1.86 billion US dollars.
Building an independent supply chain takes more than ambition. Solar cell factories require substantial capital, technological partnerships, and lengthy commissioning periods. China dominates the global market for solar manufacturing equipment and technology, and Indian firms say Beijing’s export restrictions on related technology have made it even harder for India to expand its own capacity.
The clock is ticking on India’s climate commitments. Indian solar companies warn that the cell shortage is driving up costs, delaying project construction, and could jeopardize the country’s 2030 target of installing 500 gigawatts of non-fossil fuel energy capacity. Currently, India’s non-fossil energy capacity stands at about 288 gigawatts, with solar accounting for roughly 29% of that total.
The human cost is mounting too. Industry insiders say Indian solar project costs could rise by about 35% until domestic cell capacity expands. Companies reliant on photovoltaic manufacturing are also facing employment pressures. In Gujarat state alone, module manufacturers lacking cell production capacity account for roughly 45,000 jobs.

Deep Throat
** 博客文章文責自負,不代表本公司立場 **
Richard Nixon resigned in disgrace back in 1974. The Watergate scandal made him the poster child for American political corruption. More than half a century later, the man who once insisted “I’m not a crook” has become an unlikely idol for a new generation of conservatives. Call it “Nixonmaxxing”: internet slang for taking Nixon fandom to the extreme. He is going viral all over again, recast as a pioneer of “America First.” The rebranding echoes President Donald Trump’s own style, and some are now calling the long-dead Nixon Trump’s “newest ally.”
Nixon addresses the nation on Watergate, August 15, 1973.
Social media feeds are flooded with archival Nixon footage. Editors chop it into short clips and memes, set to music from popular American rappers, and the results spread fast. Young conservative influencers now wear merchandise printed with slogans like “Pretty Girls for Nixon” and “Nixon Now More Than Ever.” Baseball caps, T-shirts and fanny packs bearing the word “Nixonmaxxing” have reportedly sold out within hours of hitting shelves.
“I’m not a crook” is trending again. Gen Z conservatives have made Nixon their idol.
From Disgraced President to Meme Icon
The Wall Street Journal reported on July 17 that young American conservatives are using TikTok and Instagram to recast Nixon as a pioneer who stood up to the establishment and the “deep state.” Some American netizens have even joked online: “Is Nixon staging the greatest political comeback in history?”
Five men broke into the Democratic National Committee headquarters in 1972 to plant wiretaps, and got caught in the act. The investigation that followed traced the break-in back to Nixon’s re-election campaign. Nixon then tried to cover it up and failed, triggering the scandal that bears the building’s name to this day.
The Simpsons has mocked Nixon for decades.
Ultimately, Nixon resigned in 1974, the only US president in history to leave office mid-term. Several of his aides went to prison, and Congress passed a wave of reforms curbing presidential power. Nixon and Watergate have served as staple material for American political satire ever since.
Nixon has long been a punching bag on American television. The Simpsons mocked him repeatedly over the years. Futurama went further, portraying him as a disembodied head floating in a jar. The 1999 comedy Dick built its entire plot around Watergate, poking fun at Nixon throughout.
Futurama’s take: Nixon, reduced to a head in a jar.
The anonymous administrator behind the Instagram account “Kawaii Richard Nixon” told the US online magazine Arc: Religion, Politics, Et Cetera that they don’t fully support Nixon. But they believe Watergate overshadowed some genuine achievements of his presidency. “Watergate was of course wrong,” the administrator said, “but compared to some of the things happening today, it’s barely anything.”
“Nixonmaxxing” caps sold out in hours.
Nixon’s politics symbolize a bygone era, they added, one “completely different from today.” He pushed through liberal policies such as establishing the Environmental Protection Agency and the Occupational Safety and Health Administration, and advanced cancer research. The administrator estimated that most of the account’s followers are high school and college-age Gen-Zs.
The Trend Takes Hold in US Politics
The “Nixon craze” hasn’t stayed on social media. It has taken root inside American political circles. The Wall Street Journal reports that Trump has long admired Nixon. During his second term, Trump has repeatedly pushed back against the limits on presidential power that emerged after Watergate, a stance now echoed by senior Republican figures.
Trump wrote to Nixon in 1982, calling him “one of this country’s great men”. The two kept up a correspondence for years afterward, bonding over shared grievances. Nixon wrote back in a 1990 letter: “Dear Donald — I know nothing about the intricacies of your business enterprises but the massive media attack on you puts me in your corner!”
Vance made the connection explicit in June. Speaking at an event at the Nixon Presidential Library in California, he said: “I think that his historical legacy is enjoying a bit of a renaissance, but I think deservedly so.”
Vance, at the Nixon Library in June: Nixon’s legacy is having “a renaissance.”
Vance then compared Nixon’s 1972 election victory directly with Trump’s. He argued that the coalition returning both men to the White House was broader and more durable than the one that elected Ronald Reagan. “you know Reagan could not have won his landslide in 2024,” Vance said, pointing to America’s changing demographics, “Richard Nixon maybe could have won his landslide in 2024.”
To some in this new generation of conservatives, Nixon is no longer labelled as Watergate’s villain. He has become an “America First” pioneer instead, a tough president loathed by the mainstream media, besieged by investigative agencies, and ultimately brought down, in their view, by the “deep state.”
Years of Rebranding Behind the Scenes
None of this happened by accident. The Nixon Foundation has quietly run a years-long rebranding campaign, working to re-present the former president’s “historic achievements.” Those include the Apollo moon landing and the Sino-US rapprochement.
Jim Byron, chairman of the Nixon Foundation, said planning began back in 2018. The goal was to repackage the more than 46 million documents and 3,700 hours of recordings held at the Nixon Presidential Library into short-video content. Once the foundation started posting clips on Instagram and TikTok, “We really saw it explode,” Byron said. Some clips have racked up millions of views on YouTube.
A 2024 short video posted by the foundation, with selected footage from the old TV series “Mad Men” that features the 1960 Kennedy-Nixon race, went viral on X. The clip opens with the character Don Draper’s assessment of the two presidential candidates, waxing poetically about “the Abe Lincoln of California, a self-made man,” before concluding: “Kennedy, I see a silver spoon. Nixon, I see myself.”
The footage then cuts to photographs of Nixon set to music by an American rapper. In an accompanying recording, Nixon can be heard saying: “And believe me, when you’ve gone through the fires of having to work your way through school, of having to fight campaigns with no money, of having to do it on your own, you’ll come out a pretty strong man, and you’re not in awe of anybody.”
Netizens have flooded the comments with praise. Many call Nixon “one of the most underrated presidents,” while others say “we were probably too hard on Nixon.”
Matthew Foldi, an editor at the conservative outlet Washington Examiner, said the Nixon Foundation once gave him a limited-edition baseball cap. It was printed with Nixon’s iconic V-for-victory hand gesture.
A Mirror to America’s Political Mood
Nixon’s renewed popularity taps into something bigger. It reflects the hard-edged populism and nostalgia that have taken hold on the American right in recent years.
Conservative commentator Shashank Tripathi argues that Nixon represents “heterodox middle finger to the establishment”. That is precisely what appeals to the young conservatives championing him. In their eyes, Nixon is no longer merely the “crook” of Watergate. He has become an early victim of the “deep state,” a framing that resonates deeply with Trump supporters.
Former Fox News host Tucker Carlson claimed in 2024 that Watergate was actually a “deep-state coup” orchestrated by the CIA and FBI. Podcast host Joe Rogan later echoed similar claims on his own show, saying “the whole thing was set up by the government”.
1982: Trump calls Nixon “one of this country’s great men.” A friendship is born.
Republican political adviser Roger Stone revealed that Trump has repeatedly asked him why Nixon chose to resign rather than fight back. Stone believes that “Nixon was in fact brought down by the same deep-state institutions that tried to bring down Trump”.
White House spokesman Kush Desai struck a similar note. He said former President Nixon exposed the betrayals of the deep state and the “fake news” media. Decades later, Desai added, President Trump continues fighting these entrenched interest groups on behalf of the American people.
The Wall Street Journal noted that those pushing to rehabilitate Nixon’s reputation see it as more than restoring the standing of one president. It is also a challenge to the system of constraints on presidential power built up after Watergate.
Since the start of Trump’s second term, the White House has taken a series of steps to weaken these constraints. These include expanding presidential control over fiscal spending, firing inspectors-general, and adjusting protections for career civil servants.
Conservative commentator Christopher Rufo has gone further still. Nixon’s importance to the right, he says, lies in the fact that “America has been essentially stuck in the year 1968.” The right, he argues, needs to launch a “counter-revolution.”
Watergate holds fresh appeal today for a simple reason. Americans can now scarcely imagine a president choosing to resign with dignity, sparing the country a long, divisive impeachment battle on the principle of putting “America first” rather than himself first.
Today’s “Nixonmaxxing” may have less to do with young Americans genuinely warming to Nixon himself. It works more as a mirror, reflecting present-day American society’s fraught relationship with its own past, its institutions, its power structures and its political order.
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A drone view shows how Todd Tracy is converting 2,600 acres of his Buttonwillow farm to solar for lack of water. Top and right are wheat and cotton.
A canal cuts through farmland in Buttonwillow. Farmer Todd Tracy is converting 2,600 acres to a solar project because the lack of water prevents him from growing some crops.

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A drone view shows how Todd Tracy is converting 2,600 acres of his Buttonwillow farm to solar for lack of water. Top and right are wheat and cotton.
A canal cuts through farmland in Buttonwillow. Farmer Todd Tracy is converting 2,600 acres to a solar project because the lack of water prevents him from growing some crops.
California’s Central Valley is one of the country’s richest farming regions, blessed with fertile soils, sunny days and a massive man-made system of irrigation canals and reservoirs.
But as climate change intensifies, droughts deepen, and water restrictions kick in, growers in much of the valley are getting less water for their fields. They also face new limits on how much they can pump from underground, forcing them to leave hundreds of thousands of acres dry in the coming years. Some are turning to an answer they never thought they’d embrace: solar.
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N.B.’s largest solar farm must first find a market for the power it wants to produce  CBC
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Richland County solar farm expansion faces pushback. Here’s what to know  The State
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Amid rising national electricity demand and climbing retail energy prices, the U.S. solar and energy storage sectors achieved several deployment and generation milestones in the first half of 2026. Data compiled by the Solar Energy Industries Association (SEIA) shows that these technologies are increasingly moving from emerging resources to primary components of the domestic power grid.
According to the trade group, grid operators from California to Texas and across the Midwest have registered record-breaking performance metrics, driven by competitive technology costs and rapid installation timelines.
1. Dominance in grid additions
During the first quarter of 2026, solar and energy storage combined to account for 91% of all new electricity generation capacity added to the U.S. grid. This represents the highest collective quarterly share ever recorded by the two technologies. Solar continues its five-year trend as the leading source of new U.S. generation capacity, while utility-scale battery storage deployment continues to accelerate.
2. Solar outpaces coal generation
In May 2026, monthly electricity generation from solar resources exceeded generation from coal for the first time in U.S. history. While solar accounted for a negligible fraction of the national energy mix a decade ago, it now supplies approximately 13% of total American electricity requirements.
3. California storage performance
On the evening of July 9, 2026, the battery storage fleet within the California Independent System Operator (CAISO) footprint discharged a record 12.99 GW of power. At its peak, this storage output covered 36% of total regional energy demand, mitigating supply strain during historically challenging post-sunset peak load hours.
4. California solar generation peaks
The following day, July 10, CAISO established a new solar generation record of 23 GW, which successfully met 72% of the afternoon electricity demand in the region. This milestone marked the third time California surpassed its own solar generation record since June 1, 2026.
5. Multi-record trends in Texas
The Electric Reliability Council of Texas (ERCOT) grid also logged multiple renewable energy and storage milestones throughout the spring and early summer:
Texas currently serves as the fastest-growing utility-scale solar market in the United States and holds the position of the second-largest energy storage market.
6. Broad geographic expansion
While California and Texas continue to lead in raw volume, major regional transmission organizations (RTOs) across the country recorded independent solar generation peaks. Over the last three months, the Southwest Power Pool (SPP), ISO New England (ISO-NE), the Midcontinent Independent System Operator (MISO), and the PJM Interconnection all established new solar generation records. Notably, these generation peaks coincided with record-high maximum loads on these grids, underscoring the role of renewable assets in maintaining summer grid reliability.
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There are, obviously, transmission capacity limits on the amount of new utility scale solar +BESS can be added to existing grids. Transmission capacity of existing grids can be rapidly & significantly improved by replacing existing old conductors with new higher capacity conductors and deploying other new technology. But the energy system reform that will have the most significant, rapid impact to reduce and stabilize small business and residential rate payer energy expenses is already being deployed in France and South Korea: Solar canopy Virtual Power Plants, with on-site BESS, constructed at ALL existing parking lots over 80 spaces, nationwide, within 3 to 5 years, largest lots first. No new utility monopoly transmission or fossil gas peaker plant spending required.
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Wildfire smoke limited irradiance across southern and western Ontario and the Great Lakes in mid-July, offsetting otherwise favourable high-pressure conditions. During 10-21 July, GHI around the Great lakes and down into the US Northeast were up to 10% below average. A majority of this loss
was due to the reduced clear sky irradiance which was down over 6% in areas, according to analysis using the Solcast API.
The muted irradiance result followed a rapid increase in wildfire activity across northern and north-western Ontario. More than 100 fires were active by mid-July, later rising above 180, as Ontario’s firedanger reached “extreme”. Smoke travelled south-east across southern Ontario and Quebec before extending into the US Midwest and Northeast. Toronto, Chicago, Detroit and Minneapolis recorded some of the world’s poorest air quality during 14–17 July, while alerts were issued across more than 20 US states.
Particulate matter data (Copernicus/CAMS PM10) showed the smoke extending well beyond the source of the fires on 17 July. The highest particulate concentrations were located over north-western Ontario, with elevated particulates continuing south-east across the Great Lakes and towards the US Northeast.
The smoke is most clearly visible in the clear-sky irradiance anomaly, where aerosols reduced the available solar resource before cloud impacts are considered. The observed GHI anomaly shows a similar reduction in irradiance across the Great Lakes and down the US East Coast during the peak of the smoke event.
The smoke is most clearly visible in the clear-sky irradiance anomaly, where aerosols reduced the available solar resource before cloud impacts are considered. The observed GHI anomaly shows a similar reduction in irradiance across the Great Lakes and down the US East Coast during the peak of the smoke event.
Within the smoke corridor, particulate (PM10) concentrations over Toronto and southern Ontario remained relatively stable during 1–14 July, before increasing abruptly on 15 July and reaching around three times the earlier July baseline during 16–18 July. Concentrations then declined by 19–20 July. Closer to the fires in north-western Ontario, particulates reached approximately eight times its pre-event baseline on 17 July. These airborne particles attenuated incoming sunlight even where cloud cover was limited, reducing actual GHI relative to the available clear-sky resource.
The combination of elevated particulates and rain-free conditions exacerbated soiling losses. Soiling accumulation increased due to the smoke particulate, increasing losses rapidly, and were not cleared until much needed rainfall on the 18th July.
Solcast produces these figures by tracking clouds and aerosols at 1-2km resolution globally, using satellite data and proprietary AI/ML algorithms. This data is used to drive irradiance models, enabling Solcast to calculate irradiance at high resolution, with typical bias of less than 2%, and also cloud-tracking forecasts. This data is used by more than 350 companies managing over 300 GW of solar assets globally.
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
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Efficiency, stability and scalable deployment of organic photovoltaics  Nature
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Due to the falling price of silicon solar cells, as well as their manageable size, the sight of solar panels on household rooftops has become a familiar one. However, solar energy, as many other sustainable forms of energy, is intermittent. At peak moments, such as on sunny summer days, the grid is flooded with solar energy, while during the evening and in winter, when energy tends to be most in demand, supply is less. In order to secure energy availability on demand, storage needs to be integrated into the grid.
The pumped hydroelectric storage plant is one example of widespread technology for storing energy by pumping water from a lower reservoir to a higher one, and electricity is generated on demand by letting the water flow back through turbines. The expansion of these plants, however, is limited by the amount of suitable geographical sites. A technology that does not bear these constraints is the electrochemical battery. Lithium-ion batteries present in everyday electronics are examples of these. On a larger scale, companies are starting to invest in large batteries supporting the grid. The dominant technology for such large batteries is currently lithium iron phosphate (LFP). A report by the International Energy Agency (IEA) predicts lithium demand to multiply by more than 40 times in 2040 compared to 2020, largely caused by this need. Lithium mining, however, comes with considerable geopolitical and humanitarian concerns. If lithium mining has a significantly negative impact on local health, water supply, biodiversity and soil, can we maintain that it’s helping us to move towards a sustainable future?
Another alternative is thermal energy storage, in which energy is stored in the form of heat.
Due to the high energy density, abundance and low cost of storage materials, thermal storage seems very promising for longer duration storage. This is the principle behind solar water heaters. On a bigger scale, it is mainly used in combination with concentrated solar power plants (CSP), such as the Crescent Dunes Solar Energy Project in the USA or Andasol Solar Power Station in Spain, for example. In these installations, the first step is to convert solar energy to heat using large mirrors, either parabolic or smaller flat ones tracking the Sun, which concentrate sunlight and heat a large reservoir of molten salt. The second step consists of converting heat into electricity: the heat stored in the salt powers a turbine.
Thermal storage implemented in this way has remained marginal, because the turbines used in CSP become more efficient as they become larger, meaning that the concept is generally not cost-effective at lower scales and therefore requires very large plants, involving big startup investment and maintenance costs. Even though smaller CSP plants with integrated thermal storage exist, such as the linear Fresnel plant at Llo in southern France, they remain even more uncommon.
The efficiency of thermal storage poses a challenge, since heat is notoriously difficult to convert to “useful” energy, including electricity: the efficiency of this process is fundamentally limited by what is known as Carnot’s law. For example, at 300°C, the theoretical maximum conversion efficiency is about 50%, meaning the actual efficiency is going to be even lower. In comparison, lithium-ion batteries, which are not limited by the same physical law and have been optimised for decades, surpass 90%.
While the efficiency of heat to electricity conversion would be improved at higher temperatures, turbines hardly withstand such elevated temperatures.
However, there is another way to turn heat into electricity, namely via radiation: this is the principle behind the solar cells you see on rooftops and solar farms. Typical solar cells convert energy from the Sun. In contrast, the idea behind so-called thermophotovoltaic devices (TPV for short) is to harvest the (mostly infrared) radiation from a very hot object, where energy is stored, and turn it into electricity. For example, silicon melts at 1414°C and graphite can be heated up to more than 2000°C. At these high temperatures, the Carnot limit is pushed up to 83% and 87% respectively. In practice, experiments at MIT and the University of Michigan have now pushed conversion efficiency to beyond 40%.
The most recently developed device, which is now being introduced on the market, feature a storage medium in the form of a block of graphite or silicon, for example, heated by electricity. It is therefore similar to a lithium-ion battery but it operates by doing successive energy conversions from “electricity to heat to electricity” instead of from “electricity to chemical energy to electricity”.
However, it would be interesting to heat up our storage medium directly by the Sun, so that the device becomes a direct source of renewable energy.
In our setup at PROMES laboratory, conveniently located in the sunny south of France, the storage medium (graphite during our first tests) was heated with concentrated radiation from the Sun, using a parabolic mirror and a tracking mirror, and can be dubbed “solar to heat to electricity” or storage-integrated solar TPV (SISTPV). It’s the first prototype of a SISTPV system. An important advantage compared to CSP with molten salt storage is that the TPV battery could be cost-effective at a much smaller scale.
Although substantial work remains to improve both the efficiency of TPV cells in themselves and thermal batteries’ overall efficiency, SISTPV offers a promising avenue for supporting the transition to renewable energy. By combining energy collection, storage and electricity generation in a single, compact and potentially cost-effective device, it transforms an intermittent energy source – the Sun – into a firm one, making solar energy available on demand.

This article was originally published in French
Doctorante en physique, Centre national de la recherche scientifique (CNRS); Université de Perpignan Via Domitia
Vera Moerbeek received funding from l'École doctorale "énergie et environnement" (ED305).

Université de Perpignan Via Domitia provides funding as a member of The Conversation FR.
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Solar panels have been named as the ‘likely’ cause of a recent fire that completely destroyed a mental health hospital ward.
The Springwood unit at Malton Hospital, located in North Yorkshire, England, was set ablaze on 8 July. Local fire and rescue services say the building was “fully engulfed” when crews arrived.
All 15 patients were safely evacuated from the unit, and no injuries were reported. A full report into the blaze is expected in the coming weeks.
The fire follows three similar incidents at primary schools in England over the last 12 months, which have all been blamed on solar panels. This led to around 80 schools in the county of Suffolk switching off their solar panels as a precautionary measure.
The council says the primary school fires were all believed to have had different causes rather than a common fault with the panels.
However, it still casts a light on the potential dangers of installing rooftop solar on public buildings and residential homes.
Solar photovoltaic (PV) panels, which are individual solar cells that convert sunlight into power, have become the most competitive source of electricity in many parts of the EU.
According to Eurostat data, 47.5 per cent of the EU’s electricity consumption came from renewables in 2024, and solar PV accounted for almost a quarter of that (23.4 per cent).
The sheer boom in solar panels has increased the number of fire incidents and subsequent media attention around them. However, the per-system risk remains extremely low.
Data from Dutch research institute TNO reported that, between 2015 and 2018, only 27 fires on buildings with PV occurred in the Netherlands – with residential buildings accounting for 23 of the fires.
With 170,000 new PV systems installed on residential buildings in 2018, this represents less than 0.014 per cent of homes with solar panels.
Between 1993 and 2013, German research institute Fraunhofer ISE reported that 0.006 per cent of PV installations caused a fire with major damage. Of Germany’s total 1.3 million solar PV installations in 2013, 350 were involved in fires, while 120 were attributed to the actual cause of the blaze.
But data on solar-related fires remains sparse.
A study published last month in the journal Science Direct analysed national statistics from four European countries. It found that the number of incidents per 100,000 installations was found to be approximately 5-22 for the UK, Italy and Sweden, compared with 75 in Slovenia.
“These differences are likely influenced by variations in data collection methods and inclusion criteria across the countries,” the report says.
Researchers found that about 68 per cent of fires remained confined to the PV equipment, while 24 per cent caused damage to adjacent surfaces – such as the roof. In five per cent of cases, significant fire spread occurred, while buildings being completely destroyed only accounted for three per cent of fires.
The European Commission says that solar panel fires occur at a rate “significantly lower” than that of many common household appliances.
“When issues do arise, they are typically linked to poor installation or faulty components, not the solar technology itself,” the bloc adds.
Across Europe, strong safeguards are in place to keep solar installations safe. This includes making sure that all panels (whether they’re imported or produced in the EU) meet strict technical requirements.
“To ensure systems are installed properly, EU countries must have certification schemes (or equivalents) for solar photovoltaic installers and designers, as required by the Renewables Directive,” the Commission adds.
“These schemes include training programmes and public lists of certified installers.”
Any system that captures the energy of the sun is likely to have some level of fire risk. This is particularly true for installations that are ageing and poorly maintained.
To lower the risk of solar panel fires, only use accredited installers and do not attempt to attach panels to rooftops yourself. Wall-mounted inverters should also be bolted onto non-flammable surfaces and away from heavy debris or storage materials.
Homeowners should also have regular inspections, carried out by professionals, to check and service panels every couple of years. It is also worth inspecting your panels after extreme weather, such as strong winds, to make sure nothing has shifted.


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The world needs more critical minerals than ever before for electric vehicles, wind turbines, solar panels, batteries and smartphones.
They are all essential as part of the global shift away from fossil fuels towards cleaner energy sources  
But there is a catch. 
Many of these minerals are found in countries affected by conflict, weak governance or poverty.
If these natural resources are poorly managed, they can fuel corruption, finance armed groups, damage the environment and leave local communities no better off than before.
Those competing realities dominated a day-long debate in the UN Security Council on Wednesday, where ministers and diplomats asked a simple but increasingly urgent question: 
How can the world ensure that the race for critical minerals powers the clean energy transition without also driving conflict?
The meeting marked a clear shift in how many Member States are framing critical minerals; not only as an economic issue, but as a question of peace, security, justice and shared prosperity.
The strongest message to emerge was simple; the minerals powering the clean energy transition should benefit producing countries and local communities, not finance conflict, criminal networks or a new era of unequal extraction.
UN Secretary-General António Guterres
“Countries and communities must benefit, first and most, from the resources in their own backyard.”
“No more exploitation. No more plundering.”
China
“Countries have the sovereign right to independently choose the path of development and utilization of their natural resources.”
Colombia 
“We cannot allow this region once again to bear the environmental and social costs of extraction that colonialism taught us.”
DR Congo 
“Natural resources can be the foundation of shared prosperity or the engine of predation. Everything depends on how they are governed.”
Liberia
“A diamond can fund a classroom, or it can fuel a militia. A forest can sustain a community, or it can finance a war. Geology does not choose. Governance does.”
Pakistan
“The strategic importance of natural resources is increasing as the world transitions towards clean energy, artificial intelligence, advanced manufacturing, and digital technologies.”
Somalia
“While the security dimension is critical, the African perspective also highlights that minerals are a strategic asset for development, industrialization, and shared prosperity.”
International cooperation is necessary, but national sovereignty must be respected.
 
No formal Security Council resolution (which carries legal weight), declaration or binding plan of action was adopted.
However, the debate produced broad political convergence around the above priorities.
The current Security Council president, the Democratic Republic of the Congo, which hosted the meeting, proposed a Group of Friends on the Governance of Natural Resources as a possible follow-up mechanism.
The Group would bring together “interested” UN Member States, the United Nations, regional organizations, and international financial institutions, producing and consuming countries, responsible economic actors, and civil society.
Its mission would be to “sustain permanent dialogue, share best practices, strengthen existing mechanisms, and formulate in a progressive and pragmatic manner proposals to better integrate natural resource-related issues into the work of the UN.”
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Something strange is blooming beneath three solar farms in Minnesota.
And it has almost nothing to do with electricity.
The ground under most solar arrays is short grass or bare gravel.
It is kept tidy to stop fires and make repairs easy.
These three farms decided to try something else entirely.
But these three farms chose a very different path.
And what moved in has left researchers truly stunned.
Picture an open field on a cool Minnesota morning.
Rows of solar panels stretch away toward the horizon.
But the ground between them is not brown or cropped short.
It is alive with color and movement.
Purple coneflowers, goldenrod, and wild bergamot rise in thick drifts.
Bees drift from bloom to bloom in the low sun.
The whole field hums with steady, busy life.
The sites include Chisago, part of the wider Aurora project.
Aurora spreads across 16 sites in Minnesota, owned by Enel Green Power.
Each farm made the same unusual choice.
They swapped the usual turf grass for native prairie plants.
It looked simple on paper but took years of groundwork.
And nobody was sure what would happen next.
Across the whole country, a slow crisis has been building for decades.
Bees, butterflies, and other insects are our pollinators.
They help produce about 35 percent of the world’s food crops.
Yet their numbers keep falling as farms and roadsides get tidier.
The monarch butterfly has been hit especially hard.
It depends entirely on one plant, milkweed, to survive.
Monarchs lay their eggs on it, and the caterpillars eat only that.
As milkweed vanished from the Midwest, so did the monarch.
Fields were sprayed, and roadside weeds were cut back.
The plant the butterfly needed grew harder and harder to find.
A population once in the hundreds of millions shrank fast.
Finding one thriving on industrial land seemed almost absurd.
Almost, but not quite.
The sites were seeded with a native plant mix in 2017 and 2018.
The seed blends were carefully chosen, not random.
They aimed to bring back plants that native insects grew up with.
And milkweed was right there in the mix.
Over several years, the young prairie slowly took hold.
It took three or four years for the plants to fully settle in.
Native plants spread under and between the panel rows.
Some species only appeared in the fifth or sixth year.
This was no neat garden with straight, clipped edges.
It was a wild prairie returning, row by row.
The soil had been ordinary farmland for generations.
Now it was turning back into living grassland.
And the panels above kept making power.
Here is the part that really surprised the scientists.
According to one report, teams spent years studying these solar farms up close.
They came from the NREL, Argonne, the University of Minnesota, and Temple.
They study what they call ecovoltaics, which means growing nature among the panels.
The teams measured milkweed and monarch breeding right on site.
Across the sites, they found monarch eggs and young caterpillars.
The butterflies were not just passing through, they were breeding.
Once the prairie filled in, the land worked like a nature reserve.
More eggs turned up in the shade beneath the panels than in the open.
Bees, moths, flies, and wasps arrived to join them.
Conservation groups called the results some of their most hopeful yet.
A field built for power had become a monarch nursery.
The ground below had taken on a second life.
Solar power will need huge stretches of land in the years ahead.
Much of that ground could simply end up as bare gravel.
Or it could be sown into living prairie instead.
The choice will shape millions of acres in the years ahead.
The benefits also reach well beyond the fence line.
According to one account, pollinators from the sites visited soybean fields next door.
That means free pollination for nearby farms and their crops.
The good did not stay locked inside the fence at all.
Andy Grinstead of the Pollinator Partnership says such sites can do real double duty as restored habitat.
The Aurora project never set out to save the monarch.
It set out only to generate clean electricity for the grid.
The same blend of clean power and wildlife shows up elsewhere, from a solar farm fox to the seals near an offshore wind farm.
Yet on a cool Minnesota morning, orange wings catch the low light, doing both jobs at once.
© 2026 by Ecoportal
© 2026 by Ecoportal

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Australia is a rooftop solar leader, but 2.5 million apartment residents are missing out  Yahoo
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Mechanical Glass-Backsheet Photovoltaic Modules Delamination: Toward Materials Recycling  Wiley & Sons
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EL PASO, Texas (June 4, 2026) – Solar energy developers eyeing parts of southern New Mexico may have less to worry about than expected when it comes to dust. A new study led by University of Texas at El Paso researchers concludes that photovoltaic panels in Alamogordo — a region battered by frequent dust storms carrying particles from the White Sands gypsum dune field — lose only about 2 to 3 percent of their power output to dust accumulation, a rate far lower than that of solar facilities in comparable desert regions worldwide.
The findings, published in the journal Atmosphere in April 2026, carry direct implications for the economics of solar energy in the Chihuahuan Desert, the team said. Because dust-related losses at the study site are modest, and because light rainfall proved sufficient to restore panel performance, operators of solar facilities in the area may be able to clean their panels far less frequently than those at sites in the Middle East, Iran, or China — where soiling losses can reach 10 to 80 percent.
“What we found is that this location is genuinely favorable for solar energy, not just because of its abundant sunshine but because of how the dust behaves here,” said German Rodriguez Ortiz, the study’s lead author and a doctoral graduate of UTEP’s Environmental Science and Engineering Program. “The wind that brings dust from White Sands also helps clean the panels, and the gypsum itself appears to be less harmful to performance than the types of dust studied at other sites globally.”
Two natural factors appear to work in the region’s favor. Prevailing south-to-southwest winds strike the front face of south-facing panels directly, physically dislodging accumulated particles in a passive cleaning effect. Additionally, rainfall as light as 2.2 millimeters per hour was sufficient to restore panels to near-baseline performance — a lower cleaning threshold than has been documented in California, India and other solar markets. The anti-reflective coating on the panels studied may have contributed to rain’s effectiveness, pointing to a potential design consideration for future installations.
The study also found that gypsum — the distinctive mineral blown from White Sands — absorbs less light than other common dust minerals, meaning its optical interference with panel performance is inherently limited. That characteristic, combined with the region’s wind patterns and responsiveness to rain, positions the southern Tularosa Basin as a location where the solar resource and the operating environment are better aligned than previously understood, Rodriguez Ortiz said.
These factors lead to a reduced cleaning frequency, which translates into lower water consumption, less labor and meaningfully lower long-term operating costs, the team said.
“This research demonstrates the kind of place-based science UTEP is uniquely positioned to conduct,” said Thomas E. Gill, Ph.D., professor of earth, environmental and resource sciences, co-author of the study and Rodriguez Ortiz’s doctoral advisor. “Our location in the Chihuahuan Desert is not just a backdrop — it is a living laboratory, and this work shows how deeply understanding your local environment can generate insights with real economic and energy consequences for the region.”
The study was conducted at the United States Bureau of Reclamation’s Brackish Groundwater National Desalination Research Facility in Alamogordo, where the team monitored six solar panels across three sampling periods from late 2022 through spring 2024, recording 22 dust events in the process. Co-authors include assistant professor of chemistry and biochemistry Jose A. Hernandez-Viezcas, Ph.D.; UTEP researcher Alejandro J. Metta-Magana; and alumna Malynda Cappelle, Ph.D., of the Bureau of Reclamation.
The researchers recommend longer-term monitoring to capture seasonal variation through the summer monsoon and more and less dusty periods, and more detailed investigations into optimal cleaning practices.
About The University of Texas at El Paso
The University of Texas at El Paso is America’s leading Hispanic-serving university. Located at the westernmost tip of Texas, where three states and two countries converge along the Rio Grande, 84% of our 26,000 students are Hispanic, and more than half are the first in their families to go to college. With respect to research, UTEP is in the top 5% of universities in America and offers 169 bachelor’s, master’s and doctoral degree programs at the only open-access, top-tier research university in America.
Last Updated on June 04, 2026 at 12:00 AM | Originally published June 04, 2026

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Leased Tesla Solar panels stop producing, and each no-show repair adds 2 more weeks  Yahoo
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“You’re seeing state policy demonstrate its importance.”
Photo Credit: iStock
For many households, home batteries are becoming a standard energy upgrade rather than something reserved for backup during storm season.
In California, Texas, Hawaiʻi, and other high-cost electricity markets, homeowners are adopting them at a record pace to lower bills, get through outages, and gain more control over when and how they use electricity.
California, Hawaiʻi, Texas, and Arizona are among the states leading a surge in home battery installations, helped by policies that encourage storage.
The Energy Information Administration said U.S. homeowners installed a record 673 megawatts of battery capacity in the first quarter of 2026, as reported by TechSpot.
For homeowners curious about the shift, exploring EnergySage’s free tools to compare home battery storage options and competitive installation estimates can be a smart first step. 
EnergySage has teamed up with the electrification brand Qmerit to guarantee you get the best price on home battery storage solutions. Another option is Pila, which offers plug-and-play batteries priced at a fraction of what a whole-home backup system would cost.
Batteries let homeowners bank electricity when it costs less — often during sunny daytime hours — and use it later when utility rates climb. Paired with solar, battery storage can also help protect a home during outages, lower energy costs, and even move some families closer to off-grid living.
Increasing numbers of residential batteries are now being grouped into virtual power plants, allowing grid operators to manage large numbers of systems simultaneously and draw on them during times of heavy demand.
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To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Solar panels can save you more than $50k over their 25-year lifespan, and EnergySage can help you save as much as $10k on installation. Which begs the question — isn’t that worth an email or two?
That setup can improve local resilience and reduce the need for expensive, polluting backup plants. Yale E360 reported that U.S. home battery capacity tied to virtual power plants increased 153% in 2025, and one demonstration showed that a fleet of 100,000 home batteries could produce more power than a traditional gas peaker plant.
With residential electricity prices in April more than 7% higher than they were a year earlier, according to the Energy Information Administration, shifting grid use to cheaper times can offer real savings.
Batteries can help blunt the impact of rising rates while keeping lights, refrigeration, and devices running during blackouts and severe weather.
Policies at the state level are a big reason installations are speeding up.
California now places greater value on electricity sent back to the grid after sunset, while Hawaiʻi offers a one-time $400 payment for each kilowatt of installed battery capacity.
As Ari Matusiak, founder and chief executive officer at Rewiring America, told Bloomberg, “You’re seeing state policy demonstrate its importance.”
Cosmo van Steenis, a solar and storage analyst at BloombergNEF, also pointed to those state incentives as a key driver.
He said, “The fact that California, Hawaii, Texas and Arizona are incentivizing battery adoption is the main reason for the trend.”
Companies are also building business models around that momentum.
Austin-based Base Power offers lower-cost batteries and electricity in return for managing customers’ systems within a virtual power plant.
Sunrun, Renew Home, and Tesla also announced plans to combine “hundreds of thousands of home battery systems” into what they described as “the largest distributed power plant in the country.”
The change is also showing up in installer demand.
Martyna Kowalczyk, who runs the Dallas-based solar company Solartime, said, “Three years ago we would sell three batteries for 10 systems that we sold. Right now it’s more like eight homeowners out of 10 are electing to do a battery.”
As van Steenis put it, “And solar on its own is no longer as economic as it was.”
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by NATALIA MITTELSTADT | The National News Desk
WASHINGTON (TNND) — Agriculture Secretary Brooke Rollins pushed back on Sen. Kirsten Gillibrand, D-N.Y., saying that China-produced solar panels are taking over New York farmland.
Rollins testified before the Senate Appropriations Committee on Tuesday, during which she said to Gillibrand, "And let me tell you what New York is stressed out about: it's the fact that your state is taking prime farmland and turning it into solar farms. That is absolutely inexcusable," according to a video clip the secretary posted on X.
Gillibrand responded, saying, "…New York farmers, you will see that smart farmers do both — do both."
"I have visited the New York farmers two times — two times in Upstate New York," Rollins said, before Gillibrand's time ended.

Rollins followed up on her response in the post, saying, "Since time ran out, let me be clear, @gillibrandny: Why are you ignoring the issues directly hurting farmers and driving up the price of farmland in your own state?
Rollins' remarks come after she, Environmental Protection Agency Administrator Lee Zeldin, and country singer John Rich — who is the Trump administration's Special Envoy for American Landowners — sent a letter to New York Gov. Kathy Hochul (D) last month demanding answers about the state taking over farmland for solar farms.

“Nothing is more insulting to hardworking New Yorkers than Albany politicians who think they know it all and enthusiastically override local control and railroad local communities,” Zeldin told The New York Post on Tuesday.
“The recent actions of Hochul and her allies to force upstate communities to take on the burden of far more solar farms on agricultural land is on-brand elitism that New Yorkers have grown to despise.”
Small Business Administration chief Kelly Loeffler told The Post on Tuesday, “New York has two-and-a-half-million small businesses and we’re watching a state apparatus go against these small businesses in favor of 18 large solar farms that are going to only raise the cost of operating for those small businesses, which employ nearly half of New York’s population. This is an example of big government overreach. That is the promise of socialism.”

Many of the multibillion-dollar solar contracts in New York have been given to foreign companies, including Canada’s Boralex, France’s EDF Renewables, and South Korea’s Cypress Creek Renewables, according to The Post.
A spokesperson for Hochul told the media outlet that New York was a “national leader in agricultural land protections” and had “invested hundreds of millions of dollars toward protecting agricultural land since the inception of the State Farmland Protection Program in 1992.”
The state "maintains robust environmental and agricultural protections for the development of new energy infrastructure projects," the spokesperson added. "Under Governor Kathy Hochul, the state has increased its support of the Farmland Protection Program, which has now preserved more than 138,000 acres of prime farmland. New York energy project development is occurring only on land that is willingly leased by landowners."
2026 Sinclair, Inc.

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India has risen from a negligible solar player to the world’s third-largest solar market, driven by rapid clean energy growth
Twenty-five years ago, solar power was a curiosity in India, not a source of energy or strategy. Today it is the single fastest-growing pillar of the country’s power system, and India has climbed from a rounding error in global rankings to the world’s third-largest solar market, trailing only China and the United States on cumulative installed capacity and in 2025, overtaking the US outright in the volume of new capacity added in a single year.
This is not an incremental improvement. It is a structural repositioning of India within the global energy order, and the data laid out year by year tells the story better than any other.
Global solar photovoltaic capacity crossed a historic threshold in 2025, reaching approximately 2,383 GW worldwide, with roughly 510 GW added in the year alone, where more capacity was installed in twelve months than existed on the entire planet as recently as 2018. China remains the dominant force, alone accounting for around 1,200 GW of cumulative capacity, more than the next nine countries combined and adding close to 382 GW in 2025. But the data below the summit is where India’s rise becomes visible:

India has already overtaken Japan and Germany, two economies that led global solar deployment through the 2000s and early 2010s, to claim third place in the world. More strikingly, when it comes to new capacity added during 2025 rather than cumulative stock, India pulled ahead of the United States for the first time, becoming the world’s second-largest solar market by annual installation, with China’s 2025 additions running at roughly eight times India’s.
To appreciate the scale of this shift, the year-by-year trajectory needs to be seen in full:

At the turn of the millennium and for most of the 2000s, India’s solar capacity was effectively negligible, with a few isolated pilot and off-grid installations; after 2020, its near appearing in any global ranking table. The real inflexion point came with the launch of the National Solar Mission in January 2010, which set the country on a mission-mode path rather than leaving solar to incidental growth.
The pace through the first mission phase was modest by later standards. It was the revision of the national target in 2015, scaling ambition from 20 GW to 100 GW of solar capacity by 2022, backed by a planned investment of $100 billion that changed the trajectory altogether. What followed was not linear growth but compounding acceleration: capacity that took nearly four years to cross 3 GW after 2014 needed barely two years to add the next 20 GW and by FY 2025-26, a single year alone contributed a record 44.61 GW of new solar capacity more than double the previous year’s addition and itself larger than the country’s entire cumulative capacity as recently as 2020.
If we put it another way, the solar capacity has grown roughly 53 times over since March 2014 alone, from 2.82 GW to over 150 GW in barely a decade, a rate of expansion with few parallels among large economies.
Three structural shifts explain why India’s rise is a genuine competitive gain rather than simple scale-catching-up:
First: India overtook the US in annual new capacity for the first time in 2025. This matters more than cumulative rank, because it signals the trajectory going forward. The United States added roughly 38 GW of solar in 2024; India’s FY 2025-26 addition of 44.61 GW comfortably exceeds that pace, even before accounting for further US slowdowns linked to shifting federal incentive structures.
Second: the composition of India’s growth has diversified. Of the 150.26 GW installed as of March 2026, roughly 110.43 GW is utility-scale, 25.73 GW is rooftop solar, and 14.10 GW comes from PM-KUSUM and off-grid agricultural applications. This is not a story of a handful of giant desert solar parks alone as China’s growth largely is, but of a base broadening simultaneously across utility plants, commercial and residential rooftops and farm-level solar pumps, thus making the growth more resilient to disruption in any single segment.
Third: India is building the manufacturing base to match the installation base, a distinction few rapidly-growing solar markets achieve simultaneously. Roughly 98 GW of solar module manufacturing capacity was added in FY 2025-26 alone, taking India’s cumulative module production capacity to around 172 GW, with some industry estimates placing it closer to 210 GW. As a direct consequence, India’s solar module imports fell roughly threefold between FY 2024-25 and FY 2025-26. This is the Aatmanirbharta dimension of the solar story where India is not merely importing panels to hit installation targets, as many fast-growing markets do, but is simultaneously localising the supply chain that feeds them.
None of this scale materialised by luck. A sequence of policy interventions underlies each phase of acceleration: The Jawaharlal Nehru National Solar Mission (2010) provided the initial institutional push. The 2015 target revision to 100 GW by 2022 forced a step-change in ambition. Fifty solar parks of 500 MW-plus capacity each were rolled out to provide developers with pre-cleared land and evacuation infrastructure, removing one of the biggest execution bottlenecks that slows renewable projects elsewhere. The PM-KUSUM scheme extended solar directly to the agricultural sector, decentralising benefits to farmers rather than concentrating them in utility-scale plants alone.
And most recently, the PM Surya Ghar: Muft Bijli Yojana, targeting one crore rooftop solar installations, has pushed adoption into the residential segment at a scale few other countries have attempted. On the manufacturing side, production-linked incentive schemes for solar module and cell manufacturing directly targeted the import-dependency that had long been India’s Achilles heel a dependency now visibly shrinking, as the collapse in module imports demonstrates.
None of this should be thought of as India having closed the gap with China, which remains in a category of its own: its cumulative capacity is still roughly eight to ten times India’s and its single-year addition in 2025 exceeded India’s entire installed base. China’s dominance in solar manufacturing, particularly in polysilicon and wafer production, also remains structurally unmatched and global supply chains will continue to run through Chinese capacity for years regardless of India’s own manufacturing build-out.
But the comparison that matters strategically is not with China’s scale, which reflects a different economic model and decades of earlier industrial planning, but with India’s own historical trajectory and its immediate competitors the US, Japan and Germany. On that comparison, the shift is unambiguous: a country that had no meaningful presence in global solar rankings twenty-five years ago has overtaken two G7 economies in cumulative capacity and pulled ahead of the world’s largest economy in annual new installations, within a single decade of sustained policy focus.
The global solar race of 2025 is not simply a story of China extending its lead. Beneath that headline lies a genuine reordering of the rest of the field, and India sits at the centre of that reordering. From 161 MW in 2010 to over 150 GW by early 2026, from complete absence in the top ten to third place globally; from near-total import dependence to a rapidly localising manufacturing base, the trajectory is one of a country that identified a strategic opportunity and executed on it with a consistency rare in large, diverse economies.
The distance to China remains vast, and the road to India’s own 2030 non-fossil targets is not yet complete. But on the question posed at the outset, is India winning the global solar race? The twenty-five-year data record leaves little room for ambiguity.
 

© Bharat Prakashan (Delhi) Limited.
Tech-enabled by Ananthapuri Technologies
© Bharat Prakashan (Delhi) Limited.
Tech-enabled by Ananthapuri Technologies

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“Drawing 1kw from grid.”
Photo Credit: iStock
During a 104-degree day, a Tesla Powerwall 3 owner says the home battery stopped charging at 86%, raising concerns that could matter for many solar households. Mostly, what happens when extreme heat pushes backup equipment to its limits?
For people counting on batteries to trim power bills and keep the lights on during outages, a hot afternoon like this can turn into an expensive stress test.
Writing in Reddit’s r/TeslaSolar community, the homeowner said the system was still providing solar power for the air conditioner even while “drawing 1kw from grid.” The same post added, “My Powerwall 3 stopped charging at 86% around 4pm today while temperatures were around 104 F.”
The owner first tried to troubleshoot the issue and reset the setup. Then, they checked the unit and said it “measured around 125 F,” which made them think heat had triggered a cutoff. According to the post, the battery would normally have reached 100% by then and started helping meet household demand.
Adding battery storage is one of the best ways to protect your home during outages, save money on energy, and move closer to going off-grid. If you’re exploring your options, EnergySage’s free tools to compare home battery storage choices and competitive installation estimates can help, and EnergySage has teamed up with the electrification brand Qmerit to guarantee you get the best price on home battery storage solutions.
Home batteries are designed to store extra electricity for later use, which is especially helpful after sunset or during blackouts. When a battery slows or stops charging during extreme heat, homeowners may be forced to pull more electricity from the grid at exactly the time air conditioners are working hardest.
A fully charged battery can help households avoid costly evening electricity use, improve resilience during outages, and make better use of rooftop solar instead of sending as much energy back to the grid.
Direct sun, poor airflow, and high outdoor temperatures can all affect battery performance, a growing concern as heat waves become more frequent in many communities.
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Solar panels can save you more than $50k over their 25-year lifespan, and EnergySage can help you save as much as $10k on installation. Which begs the question — isn’t that worth an email or two?
For people who want backup power without committing to a larger system, Pila offers another excellent option. Its plug-and-play batteries are priced at a fraction of what a whole-home backup system would cost.
If you already have a home battery, you should document when charging problems happen and track what the outdoor temperature is at the time. To be proactive, homeowners can also ask their installer whether battery placement, airflow, wall exposure, or a shade solution could help reduce heat stress while still being mindful of safety or warranty issues.
Battery storage can store lower-cost solar electricity for later use, reduce reliance on the grid during peak pricing hours, and provide critical support when storms or heat-related outages strike.
When comparing sticker prices, it’s just as important to compare system size, siting recommendations, and installer experience in hot climates. Real-world performance matters, especially when a home is counting on that battery during the hottest part of the day.
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Clustering of distributed photovoltaic typical days, variable mode decomposition, and high-accuracy power forecasting  Frontiers
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Concord Monitor
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Energy bills are too high in New Hampshire: That was the impetus for Kevin Riley to get solar panels for his home.
The Sanbornton resident said his electric bills were running more than $200 per month — and he was financing it with money in his 401K.
Two years ago, he bought a five-kilowatt solar array system that has brought his bills down to a minimal $4 a month.
“For older folks, it’s huge,” Riley said of rising energy costs. “It’s a huge thing for us because we’re all on fixed incomes. And you get colder and you get hotter, so you use more electricity, use more things to stay comfortable.”
But the upfront cost for his panels was no small charge: He paid $23,000, which also came out of his savings. With his current electric bills so low, the panels have already paid themselves off, but he said relief was not available for the circumstances he faced.
New Hampshire fuel and energy assistance programs provide aid for people struggling to pay their bills, but rising costs and gaps in assistance continue to place a financial burden on Granite Staters.
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The New Hampshire Department of Energy administers the Electric Assistance Program and the Fuel Assistance Program to assist qualifying households with their bills and to increase their energy efficiency.
The Electric Assistance Program is a 12-month bill assistance program that offer discounts from 5% to 86%, depending on the household’s income and size. Applications are accepted year-round for electric customers serviced by a regulated utility.
The Fuel Assistance Program helps residents pay for heating costs in the colder months. It is the state branch for the Low Income Energy Assistance Program, or LIHEAP, and relies on federal funding each year to administer relief. Benefits range drastically, but the average benefit a qualifying household receives is $1,045. This year, the New Hampshire Department of Energy added $6 million in benefits and raised the minimum benefit.
Community action agencies are responsible for connecting with residents and disbursing the funds to applicants. Kristin Bertrand, the director of energy and area resource centers for the Community Action Program Belknap-Merrimack Counties, said it received 5,376 applications last year — lower than recent years, but still high compared to pre-COVID-19 numbers.
Applications opened for priority groups — people over 60, families with children under 6, and households with a previous heat emergency — on July 1, with another round opening on Sept.1 and closing April 30. Benefits are delivered no earlier than Dec.1. Bertrand said the organization also takes emergency applications.
“Weather can make a season fluctuate,” she said. “So if we have a cold snap coming in, or we know there’s going to be a snowstorm coming through the weekend, we’re prepared for a higher influx of applicants, maybe who are emergencies or going to be calling on Monday.”
Michael Tabory, chief operating officer of the Community Action Program, said the income guidelines on the assistance applications shouldn’t deter people from applying — more applicants qualify than they realize.
“People should apply because there is a possibility they’re eligible even if they don’t think they are,” he said.
The NHDOE also offers a Weatherization Assistance Program to serve low-income households that face high energy costs and don’t have the means to invest in energy-efficient home improvements. Rebates and incentives are also offered through the NHSaves program and directly from New Hampshire’s major utility companies.
Despite some help from the state and federal governments, advocates say Granite Staters struggle to afford their utility and heating costs, on top of other payments and necessities.
Sarah Kelly is head of the Energy Justice Clinic at Dartmouth College, which does participatory research to explore local issues around energy security. Qualitative data is collected through interviews with people highly burdened with energy costs.
She said federal dollars for energy and fuel assistance are an important safety net, but don’t meet the need for people also burdened with rising costs for housing, transportation and food, especially in the Upper Valley, where they specialize their work.
“It’s critical, but it’s not enough,” Kelly said. “It’s designed in an emergency situation; it’s not designed to … cover all of their fuel costs, and right now a lot of the nonprofits that provide support say that the funding doesn’t even cover one full refill for people.”
Ryan Clouthier echoes that point: He’s chief executive officer of the Community Action Partnership Hillsborough and Rockingham Counties. He said they receive more and more new faces every year. The agency provides as much support as it can for clients, but “you can only go so far with federal dollars.”
“We do see a lot of people who are exhausting their benefits earlier than normal, and so we’re looking for other avenues and other ways to try to help assist them and get them through the winter here,” he said.
New Hampshire has some of the highest electricity rates in the country, and that price is about to get even steeper come Aug. 1. The Public Utilities Commission has approved rate changes for all three investor-owned utilities and the Community Power Coalition in New Hampshire that will result in a $6 to $18 increase in monthly bills, as reported by NHPR. The price of fuel is also rising: Both fuel oil and kerosene have increased over $1 per gallon, on average, compared to rates in August 2025.
Mary Congoran runs the Contoocook Carry Community Fund that provides wood, oil, propane and other fuels to community members who ask. She says people who cannot afford to heat their homes conventionally find other means to do so, such as getting diesel from their local gas station or opening their hot oven doors, which can create health problems.
“I don’t know how some of the people that I talk to make it,” she said. “It just it fills me up that I can help at the moment.”
Some programs that can help with costs are coming online soon.
The Home Electrification and Appliance Rebates Program and the Home Efficiency Rebates Program was created through the Inflation Reduction Act, with over $34 million given to the NHDOE to administer the programs.
However, the program has stalled since the department applied in 2024, due to response wait times from the U.S. Department of Energy and trouble securing a program administrator. Thirty-four other states have also been approved for the program but have not gone online, according to research firm Atlas Public Policy.
The HEAR program is expected to launch this summer, according to NHDOE’s webpage, but the HER program has no announced launch date. It has discouraged people like Kevin Riley, the Sanbornton resident, from switching to energy-efficient technology, but the rising cost of utilities pushed his decision get ultimately get solar panels.
“It’s very frustrating,” Riley said. “It doesn’t seem like there’s a joint effort by anybody to sort of move these programs forward and use them in a prudent and an insightful way.”
He said there is a large focus on municipal clean energy, but not so much on the residential side. He hopes the eventual implementation of programs like HEAR and HER will incentivize more people in the state to invest in such technologies.
“Everybody deserves to have a safe place to live — clean, dry — and food to eat and good medical care. It’s a right. It’s not a privilege,” Riley said. “And I look at the same thing with the solar panel. We have a right to have clean air. We have a right to have a good environment. And I think they should keep pushing everybody to be doing this.”

Emilia Wisniewski is a general assignment reporter that covers Franklin, Warner and Henniker. She is also the engagement editor. She can be reached at ewisniewski@cmonitor.com or (603) 369-3307 More by Emilia Wisniewski
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“At this same time of day on a sunny day my solar array should be producing about 2550 watts.”
Photo Credit: iStock
A Minnesota homeowner bracing for heavy wildfire smoke to cripple their rooftop solar system got a surprise instead: Even under hazy skies that cut visibility to about 1.5 miles, the panels kept producing a meaningful amount of power.
Despite smoke from fires in the Boundary Waters and Canada cutting visibility dramatically, the homeowner reported in a recent post on Reddit that their system was still producing 1,075 watts — much better than the few hundred watts they had expected.
The original poster compared the smoky-day performance with that of a normal sunny afternoon.
“At this same time of day on a sunny day my solar array should be producing about 2550 watts,” they wrote. “Right now it is producing 1075 watts.”
With clear-sky production for that hour usually around 2,550 watts, the array was still operating at about 42% of its usual sunny day output even as intense smoke sharply reduced visibility.
Before checking, they had figured output might land closer to only around 300 to 400 watts — so this sheer increase they recorded was a pleasant surprise.
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Performance may drop substantially, but it does not necessarily fall anywhere close to zero.
Wildfire smoke is becoming an increasingly familiar part of summer in many parts of North America, and it affects more than air quality. It can also influence how much electricity a home solar system produces during peak daylight hours. 
Current smoke from the ongoing wildfires in Canada has affected other areas such as Chicago, with the air quality level of 753 being determined as “extremely hazardous.” These fires pose an extreme danger to people, as captured by one video of a freight train in Armstrong, Ontario that had to go through intense parts of the fire.
A system that still produces 1,075 watts in thick smoke can continue offsetting some household electricity use, especially for essentials such as refrigeration, fans, electronics, or charging devices. This may translate into significant savings on your electricity bill.
Solar output does not always fall as far as people expect when conditions look terrible outside. Partial generation during poor weather or smoke can still make a noticeable difference on utility bills.
If you already have solar, monitor your system’s production closely so you can understand how your setup performs under different conditions.
If ash buildup becomes an issue, wait until conditions are safe and follow manufacturer guidance before cleaning the panels.
Tools from EnergySage can also make that process easier. The service offers free tools that let you curate competitive bids from local installers without them obtaining any of your contact information unless you choose to work with one further — putting your privacy first and making it an easier process to transition to a solar system.
Even when smoke, clouds, or heat reduce performance, systems may continue generating enough power to soften the blow of higher grid demand and rising electricity costs.
“Visibility is about 1.5 miles right now,” the original poster wrote. Even so, they explain that their panels “producing 1075 watts” may not be the best results, but aren’t “really terrible either.”
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“I think I focused too much on what I assumed instead of checking the facts.”
Photo Credit: iStock
One homeowner’s backyard pergola became the source of a surprisingly bitter neighborhood clash after the woman next door said it was getting in the way of her solar setup.
Months later, the dispute took an ironic turn when that same neighbor put up a pergola that looked much like his.
Shortly after the homeowner’s next-door neighbor, Karen, installed solar panels on the back of her home, she raised concerns about a wooden pergola that had covered her neighbor’s patio for about five years without previous complaints.
According to AOL, she came to his door and said, “I think your pergola is blocking sunlight from my panels,” then asked whether he would consider shortening it.
To keep the disagreement from escalating, he said they went outside together to look at where the shadows actually fell. He insisted the pergola’s shade did not reach her roof, but Karen kept bringing it up in later conversations and eventually sent a certified letter requesting that he voluntarily lower or remove the structure.
Seeking a definitive answer, the homeowner contacted the city planning office and was told the pergola was properly permitted and in compliance with all rules.
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He said a city employee also told him, “Your neighbor’s solar installation doesn’t automatically limit what you can build on your own property.”
The homeowners association later tried mediation, but it did not result in any required changes, and the issue seemed to die down for a time.
Then, the following spring, Karen hired a contractor to build a pergola at her own house. The homeowner said he later found out she had shown the crew photos of his structure.
As the contractor put it, “She actually showed us pictures of your pergola and said she liked that style.”
At its core, the disagreement highlighted a common tension between two things many homeowners value: expanding clean energy and preserving the right to decide how to use their own property.
Solar panels can be a practical way to lower utility bills and reduce pollution through light exposure, but that does not mean every nearby structure is automatically a threat. Concerns about shade are legitimate, yet they need to be measured rather than guessed at from across a fence.
Outdoor shade structures can make patios safer and more comfortable during intense summer heat, while solar panels can reduce household energy costs. With thoughtful planning, both can coexist.
A full shade analysis before panels are installed can identify likely obstructions early and prevent a neighbor from being blamed later for something that should have been addressed during the design process.
Keeping permits, installation dates, photos, and any city documentation together can make a major difference if the issue ends up before an HOA or local planning office.
Mediation can help establish the facts and cool down a dispute before it hardens into years of resentment.
In the end, Karen’s own comments summed up the lesson: “I think I focused too much on what I assumed instead of checking the facts.”
Later, after enjoying the added shade herself, she admitted, “I guess I finally understand why people build these.”
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“Solar almost always makes sense financially if you get a fair price.”
Photo Credit: iStock
Faced with a brand-new roof, a soaring electric bill, and a decision about rooftop solar, a Massachusetts homeowner asked Reddit a question many people are weighing right now: Would financing panels with a loan bring costs down, or just add another financial headache?
With monthly electric charges rising from under $300 to as high as $500, the thread quickly filled with practical input from solar owners, DIYers, and industry professionals.
Posting on Reddit, the Massachusetts homeowner described a house with strong sun exposure and recent insulation improvements, then wrote, “Our most recent electric bill was 500 dollars, which was the highest ever.”
They also asked, “I guess I want to know if our electric stays between 300-500 a month, is it less expensive to have a loan for solar, or would we be hurting ourselves financially?”
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The replies largely treated the property as a strong solar prospect.
Commenters pointed to the new roof and the fact that it appears to get sun “constantly with no trees near enough to block the roof.”
One user replied, “You have a great Roof for Solar. Which means you will get a good payback.”
In the replies, people said the key issue was not only whether solar would work, but whether the homeowner could secure a fair offer.
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As one person wrote, “Yes — solar almost always makes sense financially if you get a fair price from a local contractor (or DIY).”
Homeowners who want to run the numbers can explore EnergySage‘s free tools to get quick solar installation estimates and compare quotes.
A solar loan can sometimes replace unpredictable utility bills with a more stable monthly payment — but only if the system is sized well, the financing terms are reasonable, and the homeowner takes advantage of available incentives.
This kind of decision can be especially important for families electrifying parts of their home. A heat pump may change electricity-use patterns, but it can also reduce reliance on delivered fuels such as oil. If solar helps cover that new electric demand, it can improve both household finances and energy resilience.
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A common next step is to gather as much information as possible before signing anything. That can mean reviewing 12 months of utility bills, factoring in new insulation or equipment upgrades, and asking installers to show projected savings under both cash and loan scenarios.
EnergySage’s solar map shows the average cost of a home solar panel system on a state-by-state level, as well as details on solar panel incentives for each state. Together, those resources can help readers get the best price for rooftop solar panels and access available incentives.
Adding battery storage to a solar setup is one of the best ways to protect your home during outages, save money on energy, and go off grid. Batteries can also store extra daytime solar power for later use, giving homeowners more flexibility over when they use electricity. Readers interested in that option can explore EnergySage‘s free tools for information about home battery storage options, including competitive installation estimates.
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The United States is fast approaching an inflection point in energy policy. Artificial intelligence, advanced manufacturing, cloud computing and advanced defense systems are driving electricity demand at a level that the existing grid cannot reliably meet. The next generation of data centers will require ever more constant large-scale power measured in gigawatts, not megawatts. Without change, energy is becoming the limiting factor in American economic expansion.
This urgency explains the renewed interest in nuclear energy and the continued interest in fusion. Both may play important roles. Yet even under optimistic timelines, they alone will not close the near-term gap. Expanding conventional nuclear capacity requires long permitting cycles, complex financing and fuel supply chains that are often unreliably global. Fusion remains commercially unproven.
Providentially, a technology long considered theoretical and speculative has crossed a meaningful threshold: Space-based solar energy uses orbiting satellites to capture sunlight in space, where it shines continuously and without atmospheric interference.
That energy is converted and transmitted wirelessly to receiving stations on Earth, feeding directly into the grid as steady electricity. Unlike ground-based renewables, orbital solar is not constrained by when the wind blows or sun shines. A power satellite can flexibly transmit energy to earth-bound receiving stations based on local needs, strengthening strained regional grids, supporting dense data infrastructure or supplying electricity after natural disasters.
This is not a pipe dream. Scientists at the California Institute of Technology have demonstrated wireless power transfer in orbit and successfully transmitted measurable energy to Earth. Their experiment confirmed the viability of the approach outside of laboratory conditions. The system transmits power using nonionizing microwave energy rather than lasers, operating at frequencies that do not alter molecular structures and remain within established safety standards.
The economic case for space-based solar energy is strengthening. A recent report in the journal Joule outlines a pathway toward a 10-gigahertz orbital power station capable of delivering electricity at roughly 9.4 cents per kilowatt-hour. In many states, consumers and businesses pay significantly more. Commercial plausibility for this new technology is not a distant aspiration.
China, Japan, the European Space Agency and the United Kingdom are all aware of this budding technology and are investing with long time horizons. The risk to the United States if it fails to act is structural lock-in. The nation that establishes large-scale orbital power platforms will influence standards, control key transmission architecture and anchor manufacturing ecosystems around its own industrial base. Once orbital infrastructure and supply chains are embedded, they are difficult to displace. If the United States hesitates, it may find itself operating in an energy system designed and scaled elsewhere.
Moving space-based solar energy from proof of concept to scalable commercialization will require a commitment from the federal government. Congress has a clear role to play. A well-funded multiyear demonstration program stewarded by NASA and the Department of Energy could move the technology forward. Directed funding for orbital transmission systems and ground-receiving infrastructure could validate performance under real operating conditions. Clear statutory authority for space-based power transmission would reduce regulatory uncertainty and unlock private investment.
A structured public-private commercialization framework is equally essential. Federal support for private projects should be robust and milestone-based and designed to catalyze, not replace, private capital. Launch providers, advanced manufacturers, utilities and large electricity purchasers should be collaborating among themselves and government in scaling up this new technology.
The implications extend beyond energy generation. Scaling orbital solar would require American-built satellites, American launch services, American robotics and American receiving infrastructure. It would support high-skilled manufacturing jobs across multiple states and strengthen supply chains at a moment when resilience has become a public priority. It would reinforce leadership in both space systems and the next generation of power technology.
Access to orbit is no longer the constraint it once was. Reusable launch systems developed by SpaceX have significantly reduced costs. Advances in modular assembly and lightweight materials continue to narrow technical barriers. The conditions that once made orbital solar implausible no longer exist.
Congress does not need to determine whether the science works. Its decision is whether the United States will build, deploy and control this infrastructure or depend on systems shaped elsewhere. Energy has always underwritten American economic strength and national security. The next era of that strength will depend on abundant, constant and redirectable electricity.
The power race is moving to orbit. The United States should win it.
Brigitte Bren is a trustee of Caltech, a lawyer and a producer of Bright Harvest: Powering Earth from Space. ©2026 Chicago Tribune. Distributed by Tribune Content Agency, LLC.
Copyright 2026 Marin Independent Journal. All rights reserved. The use of any content on this website for the purpose of training artificial intelligence systems, algorithms, machine learning models, text and data mining, or similar use is strictly prohibited without explicit written consent.

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Solar panels have been named as the ‘likely’ cause of a recent fire that completely destroyed a mental health hospital ward.
The Springwood unit at Malton Hospital, located in North Yorkshire, England, was set ablaze on 8 July. Local fire and rescue services say the building was “fully engulfed” when crews arrived.
All 15 patients were safely evacuated from the unit, and no injuries were reported. A full report into the blaze is expected in the coming weeks.
The fire follows three similar incidents at primary schools in England over the last 12 months, which have all been blamed on solar panels. This led to around 80 schools in the county of Suffolk switching off their solar panels as a precautionary measure.
The council says the primary school fires were all believed to have had different causes rather than a common fault with the panels.
However, it still casts a light on the potential dangers of installing rooftop solar on public buildings and residential homes.
Solar photovoltaic (PV) panels, which are individual solar cells that convert sunlight into power, have become the most competitive source of electricity in many parts of the EU.
According to Eurostat data, 47.5 per cent of the EU’s electricity consumption came from renewables in 2024, and solar PV accounted for almost a quarter of that (23.4 per cent).
The sheer boom in solar panels has increased the number of fire incidents and subsequent media attention around them. However, the per-system risk remains extremely low.
Data from Dutch research institute TNO reported that, between 2015 and 2018, only 27 fires on buildings with PV occurred in the Netherlands – with residential buildings accounting for 23 of the fires.
With 170,000 new PV systems installed on residential buildings in 2018, this represents less than 0.014 per cent of homes with solar panels.
Between 1993 and 2013, German research institute Fraunhofer ISE reported that 0.006 per cent of PV installations caused a fire with major damage. Of Germany’s total 1.3 million solar PV installations in 2013, 350 were involved in fires, while 120 were attributed to the actual cause of the blaze.
But data on solar-related fires remains sparse.
A study published last month in the journal Science Direct analysed national statistics from four European countries. It found that the number of incidents per 100,000 installations was found to be approximately 5-22 for the UK, Italy and Sweden, compared with 75 in Slovenia.
“These differences are likely influenced by variations in data collection methods and inclusion criteria across the countries,” the report says.
Researchers found that about 68 per cent of fires remained confined to the PV equipment, while 24 per cent caused damage to adjacent surfaces – such as the roof. In five per cent of cases, significant fire spread occurred, while buildings being completely destroyed only accounted for three per cent of fires.
The European Commission says that solar panel fires occur at a rate “significantly lower” than that of many common household appliances.
“When issues do arise, they are typically linked to poor installation or faulty components, not the solar technology itself,” the bloc adds.
Across Europe, strong safeguards are in place to keep solar installations safe. This includes making sure that all panels (whether they’re imported or produced in the EU) meet strict technical requirements.
“To ensure systems are installed properly, EU countries must have certification schemes (or equivalents) for solar photovoltaic installers and designers, as required by the Renewables Directive,” the Commission adds.
“These schemes include training programmes and public lists of certified installers.”
Any system that captures the energy of the sun is likely to have some level of fire risk. This is particularly true for installations that are ageing and poorly maintained.
To lower the risk of solar panel fires, only use accredited installers and do not attempt to attach panels to rooftops yourself. Wall-mounted inverters should also be bolted onto non-flammable surfaces and away from heavy debris or storage materials.
Homeowners should also have regular inspections, carried out by professionals, to check and service panels every couple of years. It is also worth inspecting your panels after extreme weather, such as strong winds, to make sure nothing has shifted.


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