The sun now generates more electricity for U.S. consumers than coal, despite President Donald Trump’s penchant for carbon-heavy power. Solar power rose to become the third largest electricity source in the U.S. last month, according to a Wednesday report from energy think tank Ember. Ember’s updated statistics come days after Trump invoked the Defense Production Act and other tools to provide $700 million to support the coal industry, all in an effort to reduce energy prices spiked by the war in Iran. “Overtaking coal for the first month on record shows just how far solar has come, from a niche contributor to the third-largest and fastest-growing source of power in the U.S. electricity system,” Nicolas Fulghum, Senior Data Analyst at Ember, said. “From Texas to California, markets across the U.S. are betting on solar to meet rising power needs.” The share of solar in the U.S. nearly doubled since 2021, rising from 5.4% of energy generation capacity in May of that year to 12.8% last month. Coal, meanwhile, plummeted from 19.7% to 12.2% of American energy last month. Coal, which fueled the U.S.’s industrial rise, generated its smallest share of national power on record in April. Solar’s growth in the American market is partially due to government incentives. While Trump’s One Big Beautiful Bill eliminated Biden-era subsidies for green energy, which he called a “giant SCAM” in a June 2025 post on Truth Social, various state governments continue to offer tax credits for businesses and homeowners who install solar panels. The Biden administration pumped more than $7 billion into solar generation capacity through the “Solar for All” initiative alone. That initiative, announced in April 2024, sought to bring solar to more than 900,000 low-income households nationwide. Trump’s Environmental Protection Agency moved to cancel those grants in August 2025, with only $53 million of the $7 billion budget having been spent. Trump has long admired what he refers to as “beautiful clean coal.” THE MAN LEADING TRUMP’S NUCLEAR RENAISSANCE His energy secretary, Chris Wright, is pushing ahead on an ambitious project to build a coal export terminal on a former military site in Oakland, California. The West Gateway Terminal would provide the capacity to ship more than 12 million tons of American coal to the Asian market, though the project is expected to be mired by legal challenges. Solar now trails gas and nuclear as the third-largest source of electricity in the U.S.
Northwest Suburban High School District 214 school board members Thursday inked a deal to place solar panels atop Rolling Meadows High School, amid looming expiration of federal incentives and rising energy costs from the standard grid. The school could offset 65% of its annual electricity usage, officials estimate, which is higher than an earlier 37% projection when they began exploring the possibility last fall. Superintendent Scott Rowe — who oversaw installation of solar panels at his last district, Huntley Community School District 158, in 2019 and 2020 — suggested District 214’s five other high schools and district headquarters could get panels in the future, too. “If we might be able to get more from our coverage than we originally thought, there is an opportunity for us to continue exploring this,” Rowe said. “This is an area that we should continue exploring and pushing the boundaries on. … We can just lock in and reduce our bill, so that way our dollars can go toward the actual learning.” Under a so-called power purchase agreement approved Thursday night, Chicago-based Verde Solutions will install the solar arrays at no upfront cost, while offering the district a fixed rate for solar power over 25 years. The district is currently paying a little under 8 cents per kilowatt-hour for electricity, but will pay about half that — just under 4 cents per kWh — with solar. Technically, that’s $0.078 per kWh on the grid, versus $0.0379 per kWh for solar. It translates to a $3.4 million savings over the 25-year contract, based on current rates and assuming future adjustments for inflation, according to consultant Nania Energy Advisors. Becky Thompson, a senior energy adviser at Nania, which also advises the school district on its electricity and natural gas purchases, said solar presents a stabilized cost that will allow officials to budget year over year, while grid power continues to go up considerably. “I’m in the market every single day watching energy prices,” Thompson said. “With data center integration, electrification of vehicles, manufacturing moving to automated systems, currently our demand is outpacing our supply, which is really the simplest way to explain why energy prices continue to go up.” While the district and its consultant have been exploring solar for about a year, officials are now staring down a deadline to capture federal incentives before they expire. Solar installations need to be done by the end of 2027 to take advantage of the incentives, officials said. After putting a request for proposals out on the street in October, five solar vendors submitted plans and were ranked by a district evaluation team according to price, technical qualifications and experience. The top three were invited for interviews in December. Verde, the recommended installer, was founded in 2012 by Christopher Gersch, who is a 2000 Rolling Meadows High School graduate. Aaron Raftery, a solar expert with Nania, said that connection didn’t factor into the selection team’s scoring criteria. Verde has done 2,800 projects across 48 states, including a few schools in Illinois, some municipal governments, and many commercial projects, Raftery said. The solar arrays, which are owned by Verde, will be installed on a racking system atop a slip sheet roof membrane. The panels won’t be anchored to the school roof, so there is no impact on the roof warranty, Raftery said. It’s a 59-week installation process, from engineering to when the system can be turned on. That’s expected before school starts in fall 2027. As part of the contract, Verde will offer six annual student internships over five years, a 10-year scholarship program, and other learning experiences at the school.
HVR Solar Ltd is establishing a 1.2 GW TOPCon solar cell manufacturing line in Uttar Pradesh through strategic MoUs signed at SNEC PV Power Expo 2026. Partnering with global technology providers, the facility aims to boost India’s domestic renewable energy supply chain, reduce import reliance, and create over 500 local jobs. Listen to this article in summarized format Unlock AI Briefing and Premium Content
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At a time when the Centre is aggressively promoting rooftop solar power as a tool of energy se curity, economic resilience and climate action, the Goa government has allowed its own solar subsidy scheme to lapse, leaving hundreds of applicants stranded while a promised extension awaits official approval. The State subsidy for roof top solar installations has remained in abeyance since March 31, 2026, despite Chief Minister Pramod Sawant as suring the Legislative Assem bly during his Budget speech on March 6 that the scheme would be extended for an other three years. More than four months later, however, the promised notification has yet to be issued. The delay has plunged Goa’s rooftop solar pro gramme into uncertainty, with several applications submitted after March 31 caught in administrative limbo. Goa Energy Devel opment Agency (GEDA) of ficials have acknowledged that benefits under the revised scheme cannot be processed until the govern ment formally approves and notifies the extension. GEDA Sources said the Department of New and Re newable Energy has already written to the government seeking continuation of the scheme, while the GEDA has framed a revised policy. Yet the file continues to await approval, stalling installa tions and creating uncer tainty among consumers and vendors alike. “It is absurd that the State government is not pro moting the shift towards self-sufficiency, especially in the clean energy sector, at a time when the country is grappling with the West Asia crisis,” said John Dias from Mapusa. The policy paralysis threatens to undermine years of progress in rooftop solar adoption. According to official figures, 3,928 consumers are al ready registered for rooftop solar systems in Goa, while solar photovoltaic power plants have been installed on 48 government buildings across the State. Until the end of the last financial year, Goa offered one of the most attractive rooftop solar incentive structures in the country because consumers could avail themselves of sub sidies from both the Central and State governments. (See graphic) For many households, the economics were compelling. A typical 10 kW rooftop solar system costs about Rs 6 lakh. Under the earlier subsidy regime, a consumer could receive approximately Rs 78,000 from the Centre and around Rs 2.25 lakh from the State, reducing the effective investment to nearly half the original cost. Given Goa’s electricity tariff structure, a household consuming around 1,000 units of electricity every month would typically pay power bills of about Rs 6,500 a month, or nearly Rs 78,000 annually. With both subsidies availa ble, the investment could generally be recovered within four years. Without the State subsidy, however, the payback period almost doubles, significantly weakening the financial in centive for prospective adopters and threatening the rapid growth witnessed in recent years. Vendors however are hopeful that the government will extend the State subsidy. “The consumers who have installed panels during this financial year have not yet realised that the subsidy is not coming because it usually takes about four to six months to be disbursed. In a couple of months, the news will spread, and that will completely kill the momentum,” said a stake holder from the solar industry. Industry players say the government risks squandering a rare success story in public policy. Founding Director of Anmax Energy Anant Kochhar said, “Solar power is economical and clean. We generally recom mend that consumers install a 10 kW rooftop solar system at their residences.” According to empanelled vendors, rooftop solar systems start becoming financially attractive when household con sumption exceeds 200 units per month. For consumers us ing more than 300 units, the economic case becomes even stronger. Sanket Angle of J N Solar Power LLP said awareness about rooftop solar systems has grown substantially in re cent years. “The State and Central subsidies have helped drive the market penetration of solar panels. The subsidies are usu ally released within six months,” said Angle. “Even during adverse weather conditions, solar panels are capable of generating nearly 50 per cent of their usual power output,” he added. Sun360 founder and CEO Anish Sousa said consumers continue to derive significant long-term value from invest ing in rooftop solar systems. “The State subsidy has undoubtedly accelerated adop tion, but rooftop solar remains a financially attractive investment for many households. With electricity costs expected to rise over time and solar systems lasting more than 25 years, consumers continue to benefit from sub stantial long-term savings,” he said. “We have installed more than 1,000 solar systems across the State, and many consumers have already recovered their investment through savings on electricity bills,” he added. “The way the net metering system works is that, at the end of the financial year, the units consumed from the grid are adjusted against the units generated by the solar pan els on your rooftop. If the units generated exceed the units consumed, the government purchases the surplus power at the rate of Rs 3.8 per unit,” explained Sousa. “If the consumer opts to go partly off-grid, they would need to invest in batteries and an inverter, which for a 10 kW system would typically cost an additional Rs 3 lakh. This makes particular sense when there are different tariffs for different times of the day. Consumers can then use their battery reserve during peak tariff periods and switch back to grid power when rates are lower,” Sousa added. On the lifespan of each component, Sousa said, “General ly, solar panels last for about 30 years, while batteries and inverters have a lifespan of around 10 years each.” Ayrito George from Ambaji-Fatorda, who has installed a rooftop solar system, said, “I have installed rooftop solar systems at both my residence and my shop, and the elec tricity bill is minimal. I receive monthly bills of around Rs 250 for my house and Rs 290 for my shop. I invested about Rs 6.45 lakh, of which nearly 50 per cent was recovered through government subsidies.” Atmaram Desai from Sankhali said, “If I produce excess power, the government purchases the surplus electricity generated by me at a lower rate under the net metering system.” But not everything is about economic viability. Many ear ly adopters made the switch for environmental reasons. Savio Figueiredo from Saligao said, “I installed a 4.4 kW system in January 2020. I paid about Rs 3.2 lakh at the time and received a State subsidy of Rs 1 lakh. Back then, I did it purely for environmental reasons, and I still have not got ten around to checking whether I have broken even.” The Herald Group encompassing its flagship O Heraldo daily, Dainik Herald (Marathi Daily) Herald TV and the universe Herald Digital. We are a 360 degree media company with an integrated news and content gathering and dissemination network.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 16, Article number: 17416 (2026) Cite this article 1514 Accesses Metrics details 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 sectors1,2. 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 dust6,7,8. 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 India11,12, 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 travel14,15. 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 experiments36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55. 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 (Isc) 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 ISC as mention below Eqs. (3) and (4) as, Where Isc soiled is the short-circuit current of the soiled panel and Isc 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) wn are representing weights corresponding to each inputs xn 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 Zn represents a partition of the feature space, Cn is the mean target value within that region, and I (X∈Zm) is an indicator function that equals 1 if X belongs to Zn 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, NK(X) is the set of those neighbours, and Yi 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 mL (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 (Pmax) 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 Sampaio, P. 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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 Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar 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. 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In May, for the first time, solar supplied more of the nation’s electricity than coal, according to a global energy think tank. Even as President Donald Trump boosts coal over clean energy, solar power is hitting new milestones in the U.S. and remains the leading source of new power. Data released Wednesday by global energy think tank Ember, along with a report by the Solar Energy Industries Association and analytics firm Wood Mackenzie, show the continued growth of solar and decline of coal in the United States despite federal policy. In May, for the first time, solar supplied more of the nation’s electricity than coal, or 12.8%, Ember said. Coal supplied 12.2%, its fourth-lowest monthly share ever. “For years solar power has risen in the U.S. electricity mix,” said Nicolas Fulghum, senior energy and data analyst at Ember. ”At the same time, coal power has lost its status, first as the largest source in the U.S. mix, and then gradually over the years has fallen even further.” Solar also became the third-largest source of electricity in the U.S. in May, behind natural gas and nuclear, Fulghum said. Coal generation hit an all-time monthly low in April and rebounded only modestly in May, allowing increasing solar generation to overtake coal, he added. Electricity is produced by converting sources of energy — fossil fuels, renewable resources and nuclear — into electrical power. Burning coal, oil, and natural gas for electricity emits carbon dioxide, trapping heat in the atmosphere and warming the planet. By contrast, solar, wind, geothermal, hydropower, and nuclear are carbon-free. After about two decades of essentially flat electricity consumption in the U.S., electricity demand is increasing to power artificial intelligence, grow domestic manufacturing, and electrify transportation and heating. Fulghum said he expects to see more months when solar exceeds coal generation, before overtaking it on an annual basis in a few years. These milestones signify that solar “has staying power” at a time when there’s less support for renewable energy at the federal level, he added. Wind and solar combined have overtaken coal in the past, and wind power alone has outpaced coal during spring months when wind speeds pick up. Ember gets its hourly and monthly data from the U.S. Energy Information Administration. Globally, electricity generation from renewables is growing rapidly. Renewables will become the largest global energy source, used for almost 45% of electricity generation by 2030, according to the International Energy Agency. Last week, Trump, a Republican, announced a plan to boost the struggling U.S. coal industry by spending nearly $700 million to support coal-fired power plants and coal exports. Trump said at a White House event that “coal’s a great business” and that “in terms of power, there’s really nothing like it.” Martin Pochtaruk, CEO and founder of Canadian-based solar panel manufacturer Heliene, said Trump can say that coal is coming back but investors will invest their money in whatever brings the best return. And for power generation that is solar, making it the fastest-growing fuel, he added. A White House spokesperson defended the Trump administration’s overall energy policies, saying they were geared toward strengthening the country’s security. “The President has reversed the Left’s devastating policies, saved the American coal industry, prevented the retirement of more than 17 gigawatts of power, and saved lives during heightened demand periods,” Taylor Rogers said in a statement. While Trump is trying to reverse the coal industry’s decline, solar has been the top source for new power for five years, SEIA said. SEIA and Wood Mackenzie said solar and battery storage were practically the only energy resources being built in the first quarter, making up 91% of all new generating capacity. The Trump administration has canceled solar and wind projects, implemented policies that slowed clean energy permitting and development, and terminated $7 billion in funding intended for affordable solar energy projects across the U.S. “As power demand skyrockets, political and regulatory attacks are slowing down the exact resources we rely on,” Darren Van’t Hof, interim president and CEO of SEIA, said in a statement. “Impeding the only sector that is actively building new power is a reckless gamble that will only drive electricity bills higher.” Several groups sued the Environmental Protection Agency over canceling the Solar for All program. A district court dismissed the case last week citing lack of jurisdiction. The plaintiffs have another filing pending in the Court of Federal Claims. In a ruling Saturday, a federal judge struck down guidance from the Internal Revenue Service restricting tax credits for wind and solar projects. Trump has blamed renewable energy sources such as wind and solar power for skyrocketing energy costs. But energy analysts say recent price hikes are based on growing demand, aging infrastructure, and increasingly extreme weather events that are exacerbated by climate change. Most recently, the war in Iran that Trump launched has also led to a spike in energy costs. Blaming clean energy is “nonsensical,” said U.S. Rep. Jared Huffman. The California Democrat said that “not even lighting $700 million of taxpayer money on fire” can save the dying coal industry. “The rest of the world will move ahead toward a clean energy future with countries other than the United States leading the charge, unfortunately,” he said Wednesday. “Trump will fail in this agenda. But, he will do enormous damage to our global leadership on clean energy and to the cost of living for struggling Americans.” States won by Trump in the 2024 election accounted for 74% of all solar capacity installed in the first quarter of 2026, with Texas, Florida, Ohio, Indiana, Michigan, Arizona, and Mississippi ranking among the top 10 states for new solar additions, SEIA said. The U.S. now exceeds a total of 6 million installations nationwide across all solar sectors, which includes large-scale solar arrays, commercial, community solar, and residential or rooftop solar. Johanna Neumann, at the Environment America Research and Policy Center, said it’s “good news for our health and our planet that solar continues to grow,” and also, not surprising. “Today we can harness solar more affordably than any other energy source. It’s scalable. And it’s also our most abundant renewable energy source,” said Neumann, senior director of the center’s campaign for 100% renewable energy. “So I think it’s hard to keep the lid on a good idea, especially if the economics are tilting in your favor as well, which they are in the case of solar.” Environment America’s renewable energy dashboard shows that 32 U.S. states generated at least 10% of their retail electricity sales from solar, wind and geothermal energy last year, compared to 18 states in 2016. Clean energy in the South is booming, particularly in Florida, Arkansas, and Mississippi, Neumann said. “I think there is a misconception in the United States that clean energy is something for the coasts and liberal cities,” she said. “The true story of renewable energy is a 50-state story.”
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Reviews Clean Technologyvolume 2, pages 453–466 (2026) Cite this article 512 Accesses 4 Altmetric Metrics details Perovskite photovoltaics (PV) have delivered rapid efficiency gains; however, commercial deployment remains constrained by issues related to scale-up, reliability and system-level uncertainties. The field is now limited less by material discovery than by the complex choreography of commercialization. In this Perspective, we reframe the commercialization of perovskite PV as a multidimensional, product-centric evolution spanning materials, manufacturing, standards, policy and market design. In this Perspective, we examine perovskite and perovskite–silicon tandem photovoltaic technologies, focusing on their manufacturing maturity and commercial readiness. We highlight a plateau effect, in which additional laboratory-scale efficiency gains provide limited benefit unless accompanied by improvements in production yield, operational stability and overall factory economics. Drawing on lessons from early pilot lines, regional industrial strategies and analogue technologies such as OLEDs, we highlight the roles of supply chains, adaptive standards and risk capital in creating bankable products. Future research must treat manufacturability, stability, resource constraints and recyclability as primary design variables, and coordinated, application-driven roadmaps are essential to translate perovskite PV from record-setting devices into a credible product. Perovskite photovoltaics (PV) are no longer constrained primarily by efficiency but by the ability to integrate materials, manufacturing, standards and finance into a coherent product and value chain. Conventional performance metrics (efficiency and stability of small-area cells) are insufficient for investment and deployment decisions; technology, manufacturing and commercial readiness levels, together with bankability and warranty-compatible reliability data, must guide policy and funding. Industrially relevant manufacturing routes, robust supply chains and gigawatt-scale factory economics, such as capital expenditure, yield, throughput and learning curves, need to be treated as upfront design constraints rather than downstream optimization problems. 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A.K.H. acknowledges funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101153019. Z.-F.H. acknowledges funding from the National Science and Technology Council (114-2917-I-564-018). H.T. thanks the National Key R&D Program of China (2022YFB4200304) and the National Science Fund for Distinguished Young Scholars (T2325016); K.X. thanks the National Natural Science Foundation of China (62504100). IMEC, IUMAT, Thin Film PV Technology, Genk, Belgium Amit Kumar Harit, Zi-Fan He, Yinghuan Kuang, Tamara Merckx, Aranzazu Aguirre, Tom Aernouts & Anurag Krishna UHASSELT, Institute for Materials Research (IUMAT), Hasselt, Belgium Amit Kumar Harit, Zi-Fan He, Yinghuan Kuang, Tamara Merckx, Aranzazu Aguirre, Tom Aernouts & Anurag Krishna EnergyVille, IUMAT, Genk, Belgium Amit Kumar Harit, Zi-Fan He, Yinghuan Kuang, Tamara Merckx, Aranzazu Aguirre, Tom Aernouts & Anurag Krishna National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Frontiers Science Center for Critical Earth Material Cycling, Jiangsu Physical Science Research Center, Nanjing University, Nanjing, China Manya Li, Ke Xiao & Hairen Tan Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore Xi Wang & Yi Hou Solar Energy Research Institute of Singapore (SERIS), National University of Singapore, Singapore, Singapore Xi Wang & Yi Hou Hangzhou Microquanta Semiconductor, Hangzhou, China Buyi Yan Research and Development Center, Renshine Solar (Suzhou), Changshu, Jiangsu, China Hairen Tan Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar A.K.H. and A.K. conceived the article, collected the data and wrote the first draft of the manuscript. A.K., A.K.H. and Z.-F.H. prepared the figures. All authors contributed substantially to the discussion of the content, reviewed and/or edited the manuscript before submission. Correspondence to Anurag Krishna. Y.H. is the founder of Singfilm Solar, a company commercializing perovskite PV. H.T. is the founder, chief scientific officer and chairman of Renshine Solar (Suzhou), a company that is commercializing perovskite PV. B.Y. has ownership interests of Hangzhou Microquanta Semiconductor, a company that is commercializing perovskite PV. The other authors declare no competing interests. Nature Reviews Clean Technology thanks Yuanyuan Zhou, Teresa Gatti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions Harit, A.K., He, ZF., Kuang, Y. et al. Taking perovskite photovoltaics from promise to product. Nat. Rev. Clean Technol.2, 453–466 (2026). https://doi.org/10.1038/s44359-026-00173-2 Download citation Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1038/s44359-026-00173-2 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Malta-headquartered solar developer and operator GoldenPeaks Poland Holding has filed for Chapter 11 bankruptcy protection in the US after a severe liquidity crunch left the company struggling under nearly US$1 billion of debt. The company and 39 affiliated entities filed voluntary petitions in the US Bankruptcy Court for the Southern District of Texas on 29 May. Court filings show GoldenPeaks had less than €1.1 million (US$1.27 million) in available cash against approximately US$952 million in funded debt obligations. Get Premium Subscription The filing comes despite GoldenPeaks operating one of Poland’s largest solar portfolios, with 664MW of installed PV capacity and a development pipeline of more than 500MW. According to court documents, the company’s financial difficulties were driven by a combination of persistent grid curtailments imposed by Poland’s transmission system operator, project delays, rising supply chain costs and the collapse of Spectris Energy, its primary engineering, procurement, construction (EPC) and operations subcontractor. GoldenPeaks said the failure of Spectris Energy forced the company to separate from the wider GoldenPeaks Capital (GPC) group and establish itself as a standalone business focused on its Polish solar assets. The company’s business model had previously relied on internal support from the wider GPC group, which provided development, EPC, financing, power sales and operations services. Following Spectris Energy’s collapse, GoldenPeaks was left without an in-house operations and maintenance platform. Court filings state that the debtor entities own the solar assets but have no direct employees. The company is currently managing its entire 664MW operating portfolio through a single asset management agreement signed just 16 days before the Chapter 11 filing. GoldenPeaks’ Polish portfolio consists of 548 solar projects spread across approximately 136 special purpose vehicles (SPVs). The operational fleet is organised into nine portfolio groups with 664MWp of installed capacity, while a further five portfolio groups representing more than 500MWp remain under development. To support operations during the restructuring process, controlling shareholder Brookfield has proposed a US$162.8 million debtor-in-possession (DIP) financing package. The financing is intended to fund a four-month court-supervised process aimed at either selling the business or implementing a reorganisation plan. In the bankruptcy petition, GoldenPeaks reported assets of between US$1 billion and US$10 billion and liabilities ranging from US$500 million to US$1 billion. The Chapter 11 process will allow the company to continue operating its solar portfolio while seeking a longer-term solution for its capital structure and ownership. Brookfield also recently formed a joint venture (JV) with Japanese financial services company Mitsubishi HC Capital to own and operate renewable generation projects in Europe.
Canais Plataforma: SHANGHAI, June 12, 2026 /PRNewswire/ — GCL System Integration Technology Co., Ltd. ("GCL SI" or "the Company") has showcased its scenario-based PV solutions at the SNEC 2026 which was held in Shanghai, China from June 3 to 5. GCL SI showcased a comprehensive multi-matrix product portfolio, including high-efficiency TOPCon and BC modules, scenario-based component solutions, and mobile green energy systems. The exhibition highlighted the Company's latest achievements across four strategic dimensions: high-efficiency cells, scenario-based solutions, mobile energy, and ecosystem development. The GCL SI booth at SNEC 2026 in Shanghai showcases scenario-based PV solutions Advancing Application-Specific Solar Deployment The photovoltaic industry is evolving from scale-driven growth toward value creation through more precise and differentiated applications. To address this shift, GCL SI showcased five scenario-specific module solutions: anti-dust modules for commercial and industrial applications, anti-glare modules for airports and highways, high-efficiency modules for utility-scale ground-mounted projects, steel-frame modules for 2,000V desert PV applications, and composite-frame modules for marine environments. Covering land, sea, high altitude, typhoon zones, and corrosive environments, this scenario-based approach is designed to address the varying performance, reliability and economic requirements of different deployment environments. For extreme wind and high‑altitude environments, GCL SI's steel-frame modules offer twice the tear resistance of traditional aluminum frames, withstanding extreme wind conditions of up to 60 m/s in typhoon-prone regions while improving overall project economics compared with conventional aluminum-frame designs. For harsh high-salt, high-humidity and high-heat offshore conditions, the marine-specific modules feature double-coated glass, high-water-resistance encapsulants, and full-structure protection, passing top-level salt spray and rigorous reliability tests to solve corrosion, hot spot, and aging challenges. In the commercial and industrial distributed PV market, the S-series anti-dust modules combine high-density encapsulation, full-screen anti-dust design, and advanced sealing for enhanced reliability. GCL SI's focus on product quality and reliability was further validated by its designation as a 2026 Kiwa-PVEL TOP PERFORMER, one of the photovoltaic industry's most widely recognized benchmarks for module performance and reliability. Digitalization Supporting Low-Carbon Value Creation GCL SI, together with Ant Digital Technologies, launched "GCL Carbon Chain 3.0" at SNEC 2026 and initiated the Carbon Chain 4.0 roadmap. Leveraging GCL SI's deep industry expertise in PV manufacturing, supply chain management, and low-carbon product innovation, and supported by Ant Digital's blockchain, AI and digital technologies, Carbon Chain 3.0 establishes a product carbon footprint monitoring and verification mechanism. The platform integrates carbon footprint management across procurement, manufacturing and delivery processes, enhancing transparency and supporting low-carbon value creation throughout the supply chain. Additionally, GCL SI has enhanced its integrated "module + green certificate" service capability, offering green asset solutions for both domestic and international markets. GCL SI is also translating its application-driven strategy into international markets through targeted partnerships. In Africa, the company is collaborating with PowerChina Guiyang Engineering Corporation to develop off-grid integrated solar-storage solutions. In Latin America, GCL SI is deepening its strategic partnership with Coexito to strengthen channel presence and brand positioning. On the ecosystem front, GCL SI is working with Ant Digital Technologies and Digital China to advance digital energy ecosystem development, integrating blockchain, AI, and data governance to build a smarter, more connected clean energy future. Looking ahead, GCL SI believes the next phase of solar industry growth will be driven by solutions that deliver superior performance, sustainability, and application-specific value. The company will continue to advance technologies that support the evolving needs of global energy markets. Generalist media, focusing on the relationship between Portuguese-speaking countries and China. Subscribe Plataforma Newsletter to keep up with everything!
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Data Source Statement: Except for publicly available information, all other data are processed by SMM based on publicly available information, market communication, and relying on SMM's internal database model. They are for reference only and do not constitute decision-making recommendations. Notice: By accessing this site you agree that you will not copy or reproduce any part of its contents (including, but not limited to, single prices, graphs or news content) in any form or for any purpose whatsoever without the prior written consent of the publisher.
0 Powered by : HVR Solar Ltd, an India-based module manufacturer, has announced multiple MoUs for a 1.2 GW TOPCon solar cell manufacturing line. The facility will be located in Amroha district of Uttar Pradesh, with the agreements formalised at SNEC PV Power Expo 2026. The proposed line will be supported by international and domestic technology providers. Shenzhen Han’s Photovoltaic Equipment will supply manufacturing equipment and machinery for TOPCon (Tunnel Oxide Passivated Contact) solar cells. Gentech Technology will provide chemical and gas utility systems required for the cell manufacturing process. Indygreen Technologies has been appointed as the technology facilitator for integration and deployment of the production line. HVR Solar said the proposed facility is projected to create more than 500 direct employment opportunities across engineering, machine operations, and administrative functions.
0 Powered by : SC Solar, a China-based provider of solar PV equipment, displayed its manufacturing equipment and turnkey solutions during Shanghai’s SNEC 2026. The company’s presentation focused on perovskite single-junction and tandem technologies, BC cells and modules, along with lamination equipment. Its perovskite session detailed process stages such as evaporation, coating, crystallization, encapsulation, inline inspection, and turnkey solutions for R&D, pilot-scale, and mass-production lines. The company launched iLink Module Laminator and Optipure Clear Layer Laminator during the exhibition in Shanghai. The iLink system uses modular architecture, capacity-based combinations, and single-piece flow production for module manufacturing. Optipure adds real-time process visibility, AI-assisted recognition, and intelligent monitoring for lamination verification. For BC manufacturing, SC Solar highlighted chain-type RCA cleaning and integrated solutions for BC module production.
Live 10:00-12:00 14.49 12 Jun 2026 Share this article 14.49 12 Jun 2026 Share this article Solar panel owners have been urged to check their generation data, following a glitch that might see some of them deprived of income. There has been a recent surge in the number of households installing solar panels in recent months, with many keen to cut their reliance on expensive fossil fuels. ESB Networks has notified many solar panel owners that a technical error means their energy generation data is not being correctly displayed. On Lunchtime Live, Irish Independent Personal Finance Editor Charlie Weston said the error relates to the sunniest period of the year.
“If you went on to the ESP Networks website and looked to see what your consumption is and what you would have exported to the grid, for a lot of people, there’s no values being shown since May 25th for exports to the grid,” he explained. “We’ve had some exceptional sunshine, probably the best sunshine this year – solar panels would have been working overtime. “And this is the very period that people look to see what they’ve exported back to the grid – nothing is showing up on the ESB Networks website.”
Solar has overtaken gas power in Asia to become the continent’s third-largest source of electricity, according to new analysis by Carbon Brief. The rapid expansion of solar power in nations such as China, India and Pakistan has seen its annual output increase nearly fourfold since 2020. Asia accounts for around 60% of the world’s solar-power growth in this period, putting the continent at the heart of the global solar boom. Coal and hydropower remain Asia’s largest sources of electricity, generating roughly 52% and 12% of the continent’s power each year, respectively. Yet despite expectations that gas power would undergo “explosive growth” in the region, output has stalled due to supply disruptions, relatively high gas prices and growth in clean alternatives. In contrast, solar has surged, generating some 1,727 terawatt hours (TWh) of electricity in the 12 months to April 2026. As the chart below shows, this pushes it just ahead of gas, which generated 1,711TWh over the same period and has remained roughly flat for the past several years. The milestone reflects wider trends in the global electricity mix, with monthly generation from both wind and solar surpassing gas generation globally for the first time in April 2026. Asia’s solar expansion has been driven largely by China, which accounts for nearly three-quarters of the growth in the region’s output since 2020. Record installations in 2025 took China’s cumulative installed capacity to 1.2 terawatts (TW) by the end of the year. China also dominates global solar supply chains, hosting more than 80% of solar manufacturing capacity. This means it has played an important role in enabling solar deployment in other Asian countries through cheap solar-panel exports. Amid the energy crisis sparked by the Iran war, Chinese solar exports to Asia doubled to reach a record 39 gigawatts (GW) in March 2026. Meanwhile, Asian countries have faced a number of challenges in expanding gas-power capacity. Most of these nations are reliant on imported liquified natural gas (LNG) to support their gas-power projects. Around 81GW of planned gas capacity in Asia was cancelled in 2022 and 2023, amid LNG supply disruptions and price spikes following Russia’s invasion of Ukraine. LNG import terminals and pipelines have faced delays and cancellations in south Asia and South Korea as a result of rising fuel and construction costs, as well as weak demand for gas power. Global gas turbine shortages have also delayed plans to build new gas-power plants in Vietnam and the Philippines. While Asia’s gas-power capacity increased by 22% between 2019 and 2024, gas-fired generation has only increased by a modest 6% over the same period. Existing gas plants are not always operating at high capacities, as gas is outcompeted by other fuels. These trends are not uniform across the region, with increased generation in some countries – such as China and Taiwan – being offset by declines in others – such as Japan and India. Although China has nearly doubled its gas -power generation in the past decade, gas supply issues and high prices make it less competitive than coal and renewables. The expansion of clean energy has also reduced the need for gas-fired generation in many Asian countries. Pakistan’s widely reported “boom” in rooftop solar is one notable example of this trend. According to the International Energy Agency (IEA), the latest energy crisis has “renewed gas supply reliability and affordability concerns” among gas-importing countries in Asia, many of which are highly dependent on gas flows through the strait of Hormuz. The figures in this article are based on Ember’s monthly and annual electricity data for Asia. Annual data was used for the year-end data points, as the coverage is more complete compared to the monthly data. Rolling annual totals based on monthly data were used to interpolate between the annual data points. The figures in the chart are based on Ember’s definition of Asia, which covers the following countries: Afghanistan, Armenia, Azerbaijan, Bangladesh, Bhutan, Brunei, Cambodia, China, Georgia, Hong Kong, India, Indonesia, Japan, Kazakhstan, North Korea, Kyrgyzstan, Laos, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Pakistan, the Philippines, Singapore, South Korea, Sri Lanka, Taiwan, Tajikistan, Thailand, Timor-Leste, Turkmenistan, Uzbekistan and Vietnam. This does not include some countries that are part of the continent of Asia and that use relatively large amounts of gas, such as Iran, Saudi Arabia, the United Arab Emirates (UAE) and Russia. Analysis: Wind and solar have saved UK from gas imports worth £1.7bn since Iran war began Renewables 07.05.26 Q&A: How the UK government aims to ‘break link between gas and electricity prices’ Renewables 21.04.26 Clean energy pushes fossil-fuel power into reverse for ‘first time ever’ Renewables 21.04.26 Analysis: Record wind and solar saved UK from gas imports worth £1bn in March 2026 Renewables 02.04.26 Expert analysis direct to your inbox. Get a round-up of all the important articles and papers selected by Carbon Brief by email. Find out more about our newsletters here. Get a round-up of all the important articles and papers selected by Carbon Brief by email. Find out more about our newsletters here. Published under a CC license. You are welcome to reproduce unadapted material in full for non-commercial use, credited ‘Carbon Brief’ with a link to the article. Please contact us for commercial use.
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The California Public Utilities Commission (CPUC) is celebrating the completion of two community solar projects developed by Ava Community Energy, totaling just over 2 MW. The projects, located in Oakland and just south of Los Angeles in the city of Carson, will both be hosted or owned by real estate investment trust Prologis. At 1.32 MW and 720 kW respectively, the projects bolster the Golden State’s existing portfolio of community solar projects, CPUC officials say. “The competitively procured community solar project in Oakland creates opportunities for customers who may not be able to install solar panels on their own homes to participate in a program that delivers direct discounts to their electricity bills,” says Kerry Fleisher, a director in the CPUC’s Energy Division. “Projects like this one demonstrate how the best community solar projects can expand the number of clean energy projects in California while supporting the communities that need the bill relief the most.” The Oakland project will begin operation under the utility commission’s Disadvantaged Communities Green Tariff (DAC-GT) Program, representatives say. Now fully operational, the rooftop site will be able to provide not only renewable energy, but “meaningful bill savings” one nearby households in low income areas. The Carson project, funded by the CPUC’s Community Solar Green Tariff (CSGT) program, aims to demonstrate how future solar developers can make use of underutilized spaces for renewable energy projects. The state currently boasts one of the strongest networks of community solar projects in the U.S., having built a model that loops in renters, residents of multifamily housing, and others who may not be able to access rooftop solar. The state’s Multifamily Affordable Solar Housing (MASH) program, founded in 2008, has opened and expanded programs for community solar of all types, officials say. “These efforts continue to demonstrate that community solar is viable and scalable throughout California through the most competitive projects,” the CPUC says of its community solar programs. “Today, more than 1,200 shared solar projects totaling approximately 560 MW are operating across the state, with an additional 430 projects totaling 165 MW currently under construction.” In total, Ava’s projects will serve approximately 3,000 households through their community programs, representatives from CPUC say.
Since going online in 2024, Des Moines’ first free-standing city solar field has produced more than 1,800 MWh of electricity — enough to power 280 homes for a year, according to a recent social media post from the city. Once an unregulated dump site, the Harriet Street Solar Field now largely powers two neighboring city facilities: Animal Rescue League Animal Services, 1441 Harriet St., and a nearby greenhouse. Costing around $3 million to construct, the project provides renewable energy and marks a step toward the city’s goal of achieving carbon-free electricity, city officials said. The electricity produced by the solar field has avoided over 1,527 metric tons of greenhouse gas emissions, city officials said in the Facebook post. The panels have a 30+ year life expectancy, officials wrote. Approved in 2023, ADAPT DSM is an ambitious climate action plan that aims to reach net-zero greenhouse gas emissions by 2050 in Des Moines. The plan also seeks to make city buildings more efficient, improve access to alternative transportation, such as walking and biking, and invest in alternative forms of energy, such as solar panels for city buildings. More: Des Moines cut sustainability staff in 2025. What’s happened since? Since the city eliminated its dedicated sustainability team in 2025 due to budget cuts, the city hired contractors to help plan its first community resilience hub, a resource center residents could turn to when disaster strikes, and to work with city staff on other climate initiatives outlined in ADAPT DSM. Des Moines city staff updated City Council members about the contracts at a work session on Monday, June 8. Harriet Street Solar Field is among several other solar installations on city property. Other installations include the panels on Fire Station 11, 4150 E. 42nd St., the parking garage across from the historic Des Moines City Hall, 400 Robert D. Ray Dr., the Municipal Services Center II, 1700 Maury St., the Franklin Avenue Library, 5000 Franklin Ave., and the Reichardt Community Recreation Center, 915 College Ave. The area around the solar field also includes planting of native grasses and a pollinator lawn, a mix of low-growing plants that attract pollinators, like bees, former city architect Ann Sobiech-Munson said in 2024. (This story was updated because it contained an inaccuracy.) Virginia Barreda is the Des Moines city government reporter for the Register. She can be reached at vbarreda@dmreg.com.
Jaffrey, NH The Town of Jaffrey has partnered with New Hampshire-based solar company ReVision Energy to develop a new community solar array on top of a former municipal landfill, turning underutilized land into a long-term source of clean energy and local revenue. Construction on the project is underway, with the solar array expected to come online in early 2027. Under the agreement, the Town of Jaffrey will lease the capped landfill site to ReVision Energy for the solar installation and receive annual lease payments of $10,000, with an escalator over time. The project represents a productive reuse of land unsuitable for other development while delivering meaningful economic and environmental benefits to the community. “It’s the perfect use of land that can’t do anything else,” said Jaffrey town manager Jon Frederick. “This project generates value for the town while supporting families who need energy savings the most.” ReVision Energy, an employee-owned solar company in Brentwood, New Hampshire, is leading development and construction of the array. Founded in 2003, ReVision Energy has nearly two decades of experience delivering locally based clean energy solutions across New England. Financing for the project is provided by Blue Haven Solar, a solar financing entity of Blue Haven Initiative. Blue Haven is a family office that makes impact investments to achieve financial returns alongside positive social and environmental impact. The collaboration reflects the shared mission of Blue Haven Solar and ReVision Energy to accelerate the transition to clean energy while expanding access to solar savings for low-income communities. The 1.34 megawatt community solar array will be powered by 2,266 U.S.-assembled solar panels, and will generate more than 1.7 million kilowatt hours of electricity each year, while offsetting 933 tons of carbon pollution. Once operational, 100% of the energy produced by the community solar array will benefit some 250 low- and moderate-income households enrolled in, or on the waitlist for, the state of New Hampshire’s Electric Assistance Program (EAP). Participants are projected to receive up to $2 million in bill credits during the life of the system. Participants will be enrolled in the Energy Assistance Program for Low-Moderate Income (EAP LMI) Community Solar, allowing them to receive direct savings of 25% off the electricity supply rate in their utility bills. Member selection and enrollment will be managed by Eversource, the administering utility, and guided by criteria established by the New Hampshire Department of Energy. Priority will be given first to EAP customers and waitlist households within the project’s zip code, followed by eligible customers in neighboring zip codes, helping to ensure that energy savings accrue locally. If demand exceeds available spots within any priority category, participants will be selected through a randomized process. By converting a closed landfill into a community solar resource, the Jaffrey project demonstrates how municipalities can creatively repurpose constrained land to meet clean energy goals, support local budgets, and deliver tangible benefits to residents most impacted by rising energy costs. ReVision has worked with local communities to install solar projects on 11 landfills in New Hampshire and Maine, with three more slated for completion by 2027.
“This project shows what’s possible when communities, clean energy developers, and mission-driven partners come together,” said Mark Zankel, Director of Community Solar at ReVision Energy. “By transforming a capped landfill into a source of clean power and directing 100 percent of the energy to households enrolled in the Energy Assistance Program, the Jaffrey Landfill community solar array will transform an underused site into meaningful, long-term benefits for the community and or Granite Staters who need it most.”
The Fraunhofer Institute for Solar Energy Systems (ISE) is no stranger to solar power records, and it’s just set another one. Via its own III-V germanium solar PV module, the institute reached 34.4 percent solar module efficiency. The solar record march goes on. “The solar cells were developed by AZUR SPACE, while the anti-reflective coatings on the front glass were provided by temicon. Visitors to Intersolar / The Smarter E 2026 can see the world’s most efficient PV module at Fraunhofer ISE’s booth A1.440,” Fraunhofer ISE shares. The Fraunhofer ISE team actually just set the solar module efficiency record earlier this year before building on the tech to set a new record in recent days. The scientific details are beyond my understanding, though, so there’s no way I’m explaining what happened better than Fraunhofer ISE is explaining it. Here’s more info: “In early 2026, a Fraunhofer ISE research team working on the ‘Vorfahrt‘ project built an 833-square-centimeter module with an efficiency of 34.2 percent—a new world record. The module consists of triple III-V germanium cells, which the research project coordinator, AZUR SPACE Solar Power, further developed for the solar module. To achieve this, the manufacturer adapted its triple solar cell technology—originally optimized for space applications—to the terrestrial solar spectrum, enabling it to be produced in comparable quantities and on the same wafer formats. “A few months later, the project team has now surpassed its own achievements: By using shingled matrix technology to interconnect the solar cells, they were able to increase the module’s efficiency to 34.4 percent. For several years, Fraunhofer ISE has been collaborating with a German mechanical engineering partner to develop the interconnection of solar cells using shingle-matrix technology, which is also used in commercial modules manufactured in Germany. The shingle-matrix approach represents a fundamental departure from traditional photovoltaic module construction, in which solar cells are cut into narrow strips and then arranged in a shingle-like pattern—overlapping and offset from one another—and connected using electrically conductive adhesives (ECA). “This architecture enables direct cell-to-cell contact, thereby eliminating the need for traditional solder-coated copper ribbons. The key advantage: By eliminating cell interconnects, no active cell area is shaded. The resulting exceptionally high area utilization was a key factor in achieving the record efficiency.” Sounds cool, right? CleanTechnica’s Comment Policy Zach is tryin’ to help society help itself one word at a time. He spends most of his time here on CleanTechnica as its editor-in-chief and CEO. Zach is recognized globally as an electric vehicle, solar energy, and energy storage expert. He has presented about electric vehicles and renewable energy at conferences in India, the UAE, Ukraine, Poland, Germany, the Netherlands, the USA, Canada, and Curaçao. Zachary Shahan has 9175 posts and counting. See all posts by Zachary Shahan
Source: Xinhua Editor: huaxia 2026-06-13 15:38:32 In Bosnia and Herzegovina(BiH), the Bileca PV-plus-storage project, a landmark collaboration project between BiH and China, marks a turning point for the country’s energy transition. #GLOBALink
“Balcony solar” has been one of the more popular stories of the past year. More and more locations are allowing plug-and-play solar. No need for a permit. No need to wait for months to install solar. No need for a solar installer at all. You just plug in your solar panels and collect the energy. Now, the biggest state of all is on the verge of approving plug-in solar. If California was a country, it would be the 4th largest economy in the world, only trailing the USA as a whole, China, and Germany. It led the USA in solar power installations for a long time, but big cuts to net metering policies in the Golden State have hurt the industry massively. The California Supreme Court just decided to kill efforts to appeal the California Public Utilities Commission’s net metering cuts, but perhaps balcony solar can help boost the industry a bit. The California State Assembly’s Committee on Utilities and Energy just voted 18-0 to advance SB 868, the bill that would allow plug-in solar panels in the state. “SB 868 clears away the needless red tape that currently makes it infeasible for people to use this technology,” Senator Scott Wiener (D-San Francisco), who introduced the bill, posted following the vote. He added that this could help people to lower their energy bills, and would. naturally add more clean energy in the state. The bill has a couple more steps to go, though. It has to be passed by the Assembly Committee on Appropriations and then it has to b passed by the full Assembly. “Because the California Public Utilities Commission (CPUC) estimates the bill will result in an ongoing annual cost of between $200,000 and $500,000, it will be placed on the Appropriations committee suspense file and heard in August,” pv-magazine shares. “The California bill is one of 34 plug-in solar bills considered in state legislatures since 2025. The first such bill to be signed into law was Utah’s HB 340 in 2025, which inspired a movement toward state-level action on balcony solar legislation.” It’s true. Whether the name catches people, or just the freedom and lack of red tape, this idea has caught on like wildfire and is popular well beyond typical solar enthusiast circles. If it becomes a possibility in California, perhaps it won’t bring back the ~17,000 jobs lost from the net metering cuts, but it will bring some kind of boost to solar and the economy in California. Plug-in solar is now legal in Colorado, Connecticut, Maine, Maryland, and Virginia. It’s on the verge of being legal in New Hampshire, New York, and Vermont. If California joins the party, it would be fun to see how deployments in California and New York compare. Well, California probably has the edge, thanks to tons of sunshine, 44% of the population being renters, high electricity prices, overall solar power awareness there, and the fact that it’s the most populous state in the country. However, there are a lot of apartments in New York City, and New Yorkers have been keen to show Western Conference West Coast people that they shouldn’t be underestimated lately. CleanTechnica’s Comment Policy Zach is tryin’ to help society help itself one word at a time. He spends most of his time here on CleanTechnica as its editor-in-chief and CEO. Zach is recognized globally as an electric vehicle, solar energy, and energy storage expert. He has presented about electric vehicles and renewable energy at conferences in India, the UAE, Ukraine, Poland, Germany, the Netherlands, the USA, Canada, and Curaçao. Zachary Shahan has 9171 posts and counting. See all posts by Zachary Shahan
The Global Polysilicon Marker (GPM)—the OPIS benchmark for polysilicon produced outside China—was assessed at $19.227/kg, or $0.040/W, remaining unchanged from the previous week, according to the OPIS Global Solar Markets Report released on June 9. Global polysilicon market fundamentals remain broadly stable. However, industry participants interviewed by OPIS during the Shanghai International Photovoltaic Power Generation and Smart Energy Conference & Exhibition (SNEC) last week indicated that future pricing, production strategies and sales trends will likely hinge on the outcome of the U.S.’ Section 232 national security investigation into imports of polysilicon and its derivatives. According to unofficial industry feedback, the Section 232 investigation findings have already been submitted to the White House, with July 4 now widely viewed by participants as the latest likely date for an announcement. Substantial pricing disparities persist across supply sources despite relatively stable global polysilicon prices. Market participants said price gaps between long-term contracts for non-Chinese polysilicon from different origins can reach as much as $5/kg, while spot-market differentials can be as wide as $10/kg. Despite heightened market attention on the Section 232 investigation, some industry participants believe its potential impact may be overstated. One source said the outcome is unlikely to fully block non-U.S. polysilicon from entering the U.S. market and will likely remain within a range producers can absorb. Another participant argued the more critical issue is whether the outcome can keep Chinese polysilicon that fails traceability requirements out of the U.S. market. If such material retains even limited U.S. pathways, it could still pressure sales opportunities for non-U.S. suppliers, who will likely face some tariff burden themselves. Meanwhile, after eight weeks of stability, the China Mono Premium—OPIS’ assessment for mono-grade polysilicon used in n-type ingot production—fell 1.88% week-on-week to CNY 33.429 ($4.86)/kg, or CNY 0.070/W. Market participants told OPIS at SNEC that Chinese polysilicon producers are increasingly adopting divergent business strategies, as differences in market positioning, financial strength and operating structure push companies toward individual survival strategies rather than coordinated output reductions. One established producer said its polysilicon facilities are currently operating at around 70% utilization, significantly above the industry average. The company attributed this to strong performance in its core non-polysilicon business, where order visibility extends through 2027-2028, providing financial support for its polysilicon operations. Another second-tier producer expressed a similar view, noting that diversification into other business segments, combined with a comparatively lighter asset burden than larger integrated peers, has helped sustain its polysilicon business throughout the current market downturn. Second-tier producers are increasingly offering more competitive pricing in exchange for market share and survival, a strategy that may widen supplier price disparities and add downside pressure. Global solar supply chains are being reshaped by trade barriers, industrial policy, regional manufacturing strategies, and volatility in key raw materials. This pv magazine Webinar+ will provide a detailed market analysis of how geopolitical developments are creating regional pricing disparities across the photovoltaic value chain, from polysilicon to modules and critical materials such as soda ash, EVA, and POE. Larger integrated producers are taking a more cautious approach. One leading producer said it has restarted idled capacity at minimum technical load to capture lower wet-season hydropower costs, with a more visible increase in output expected from August. Even so, the source said the return of supply is already weighing on prices, with SNEC spot discussions heard as low as CNY31-32/kg. Market participants also told OPIS that Chinese authorities have not fully abandoned efforts to guide consolidation and capacity control in the polysilicon sector. According to a market participant, the China Photovoltaic Industry Association (CPIA) convened another meeting with polysilicon producers shortly before SNEC, encouraging participants to further explore consolidation mechanisms. However, without a clear and actionable framework, many have become less willing to devote significant resources or attention to the effort. Nevertheless, some participants believe further price declines may be limited as many producers are already selling below cost. A module producer told OPIS that polysilicon is now only the fourth-largest module cost component, making further declines unlikely to materially improve project economics or revive demand. Instead, the source said additional weakness could undermine market confidence and erode supply-chain value. On exports, a polysilicon trader noted rising overseas interest in Chinese material because of its current price competitiveness, with inquiries from India and Turkey increasing during SNEC. Participants cautioned, however, that meaningful export growth is unlikely in the near term, as large-scale wafer manufacturing capacity outside China will take time to develop. OPIS, a Dow Jones company, provides energy prices, news, data, and analysis on gasoline, diesel, jet fuel, LPG/NGL, coal, metals, and chemicals, as well as renewable fuels and environmental commodities. It acquired pricing data assets from Singapore Solar Exchange in 2022 and now publishes the OPIS APAC Solar Weekly Report. 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Comments Please login to comment Thursday, July 9, 2026 11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid Thursday, June 18, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors Monday, June 1, 2026 5:30 pm – 6:30 pm CEST, Berlin, Madrid, Paris Wednesday, June 3, 2026 4:00 pm – 5:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 9, 2026 11:00 am – 12:00 pm CEST, Berlin, Paris, Madrid Thursday, June 11, 2026 5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 16, 2026 10:00 am – 11:00 am CEST, Berlin, Paris, Madrid Wednesday, June 10, 2026 3:00 pm – 4:00 pm CEST, Berlin, Paris, Madrid Friday, June 12, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid Monday, June 15, 2026 9:30 am – 10:30 am CEST, Berlin, Paris, Madrid Tuesday, June 16, 2026 6 am – 7:00 am CEST, Berlin The new pv magazine Global May issue is now available! Mountains to climb Available in print and digital formats. Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects. April 01 – August 31, 2026 A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution. Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy. Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way! We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team. June 23-25, 2026 | MUNICH, GERMANY
The Philippines’ Board of Investments (BOI) announced it certified 13 renewable energy projects under the government’s Green Lane initiative in the first five months of 2026. The country’s Department of Energy (DOE) speficied tha the 13 renewable energy projects certified during the period accounted for the overwhelming majority of the PHP 346 billion ($6.18 billion) in total Green Lane investments approved, representing 99.6% of the total project value. “Renewable energy is not a side story in our economic growth, it is the headline. The PhP 344.6 billion that investors are committing to renewable energy under the Green Lane is proof that the Philippines is a destination for clean energy business, and that Filipino workers will be the first to benefit”, Energy Secretary Sharon S. Garin said. “Every megawatt of solar, wind, hydro, and geothermal power we bring online is a community energized, and a step closer to true energy independence. We will continue to work with our industry partners to ease every barrier standing between these projects and the Filipino people they are meant to serve,” she added. The green lane certificate issued by the Board of Investments means the project will benefit from streamlined and expedited processing of permits. The accreditation follows a Certificate of Energy Project of National Significance from the Department of Energy received in July, which is given to any national energy project with a capital exceeding $59 million. The Philippines added 899 MW of solar last year, according to figures from the International Renewable Energy Agency (IRENA), taking cumulative capacity to over 3.8 GW. The Philippines’ solar market is currently dominated by ground-mounted solar projects. Figures published by the country’s Department of Energy (DOE) state the Philippines had installed 3,492 MW of ground-mounted solar by the end of last year, compared to 52 MW of behind-the-meter capacity. 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]. Comments Please login to comment Thursday, July 9, 2026 11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid Thursday, June 18, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors Monday, June 1, 2026 5:30 pm – 6:30 pm CEST, Berlin, Madrid, Paris Wednesday, June 3, 2026 4:00 pm – 5:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 9, 2026 11:00 am – 12:00 pm CEST, Berlin, Paris, Madrid Thursday, June 11, 2026 5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 16, 2026 10:00 am – 11:00 am CEST, Berlin, Paris, Madrid Wednesday, June 10, 2026 3:00 pm – 4:00 pm CEST, Berlin, Paris, Madrid Friday, June 12, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid Monday, June 15, 2026 9:30 am – 10:30 am CEST, Berlin, Paris, Madrid Tuesday, June 16, 2026 6 am – 7:00 am CEST, Berlin The new pv magazine Global May issue is now available! Mountains to climb Available in print and digital formats. Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects. April 01 – August 31, 2026 A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution. Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy. Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way! We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team. June 23-25, 2026 | MUNICH, GERMANY
0 Powered by : San Salvador-headquartered Legislative Assembly of El Salvador has advanced support for the San Matías Photovoltaic Solar Power Plant through a favorable Finance Committee ruling. The ruling authorizes a $9.6 million loan agreement with the Kuwait Fund for Arab Economic Development. The solar project is planned in San Matías and is expected to generate approximately 20,000 MWh of electricity annually. The facility will include photovoltaic panels, electrical substations, interconnection systems, access roads and operational infrastructure. Authorities stated that the project will help diversify generation and reduce dependence on hydroelectric resources during dry seasons. The development is also expected to support carbon-emission reduction and create jobs during construction and operation.
You may have noticed your electricity bill is rising. The Pennsylvania Utility Commission alerted consumers that the price of electric generation is increasing this month, just as it’s getting hotter outside, and people are using more electricity for air conditioning. Solar advocates say there’s one affordable option that could help: solar panels that you can plug into an outlet. When Cora Stryker heard about the rising popularity of plug-in solar panels in Germany, she was excited by the idea that solar can be easily installed by anyone. “It’s primarily driven by renters in urban areas, and there you have those beautiful multi-story buildings with balconies,” she said. As a climate advocate, she saw a vision of the future in what’s also called “balcony solar.” Instead of a rooftop installation, which can cost tens of thousands of dollars, plug-in solar panels are cheaper and easier to set up. Stryker co-founded a non-profit called Bright Saver last year with two ideas in mind. “First of all, energy affordability. People can’t meet their energy bills; it’s cutting into other expenses, such as putting food on the table,” she said. “The second mission or the parallel mission is climate action, clean energy.” Bright Saver is trying to bring balcony solar to the United States. Their website sells a $500 kit that includes a solar panel and a microconverter that plugs into a standard 120-volt outlet in the house. Kits are also available at retailers like Amazon and Ikea. Stryker says a Bright Saver panel, which weighs 11 pounds and looks like a flat-screen TV, can be set up on a balcony, in the yard or anywhere that gets at least six hours of sun per day and can be plugged in. “Electricity is like water; it flows in both directions,” she said. “You will plug these into your house wiring and anything you’re running off of that house wiring — your refrigerator, your TV, your router — will consume that energy on the spot.” One Brightsaver panel produces about 180 watts of power, reducing the amount of electricity a home pulls from the electric grid. Some experts say they can pay for themselves in three to five years. Many states haven’t approved of their use yet. According to a website that tracks plug-in solar legislation, five states have approved laws enabling consumers to use plug-in solar panels. Thirteen others are in different stages of considering it, including Pennsylvania. “What we’re trying to do is to keep utility bills down,” said State Representative Chris Pielli, a Democrat from Chester County. He cosponsored a bill introduced last summer into the House Energy Committee because he said demand for power continues to rise. “We should encourage every safe source of local power generation, including these small consumer-owned solar systems,” Pielli said.“Every kilowatt helps in meeting rising demands.” Since plug-in solar adds energy to the system, groups representing electrical workers and utility companies in different states have brought up safety concerns. For example, in a power outage, they fear the devices could add electricity to back to the grid and potentially electrocute workers. “The legislation does not include provisions to ensure systems are designed to automatically disconnect during power outages,” according to an email from Duquesne Light, an electricity provider in southwestern Pennsylvania, including Pittsburgh. “Without these safeguards, there is a risk that electricity could flow back onto de-energized buildings and even distribution lines, creating potential hazards for crews working to restore service and for the public.” Experts say these concerns have been solved for years. “The fact that Germany has one million solar panels and no incidents of fire or of major deaths tells you that it’s a technical issue that can be resolved,” said Shanti Gamper-Rabindran, a professor of economics and the energy transition at the University of Pittsburgh.
Earlier this year, UL Solutions, which is behind the familiar UL label on the back of electronics, debuted a certification program for plug-in solar manufacturers to address safety concerns. Utilities have other issues with balcony solar. The bill in the Pennsylvania House states that these plug-in solar panels do not require interconnection agreements with utilities, as do residential rooftop installations. This raises concerns for Duquesne Light about how plug-in systems would “safely interact with the grid and the utility’s ability to know where and how they are operating,” according to the company. But solar advocates say people with plug-in solar panels should not be required to get these agreements and other permits, because they generate far less electricity than rooftop arrays. With rooftop solar, homeowners can get credit on their electric bill for excess power they supply to the grid, called net metering. “So there are a lot of permits that happen along the way. Those take a lot of time. They add a lot of cost,” said Henry McKay of the non-profit Solar United Neighbors. At the lower end, the average residential rooftop solar installation in Pennsylvania generates more than 7 kilowatts of electricity, which is six times the 1.2-kilowatt limit set by the Pennsylvania bill for a balcony solar array. “It’s very unlikely even for the electricity you create to backfeed out onto the grid, like what happens a lot with rooftop solar,” McKay said. “Because this is so much smaller scale, your fridge is going to eat up most of that power or whatever you’re doing at home.” Neither the Public Utility Commission nor PJM, the regional grid operator that includes Pennsylvania, wanted to comment on the legislation. In the House, it’s getting bipartisan support. “I’m trying to make sure that we have as many options as possible to help keep the burden of increased electricity costs for Pennsylvanians as low as possible,” said Representative Gary Day of Lehigh County, who is one of three Republican co-sponsors of the bill. Even though House Republicans have opposed solar programs in the past, he said they support an “all of the above” energy strategy. The bill’s Democratic sponsors say if the plug-in solar bill doesn’t pass this time around, they will keep reintroducing it. 12 June 2026 Episodesolar Julie Grant got her start in public radio at age 19 while at Miami University in Ohio. After studying land ethics in graduate school at Kent State University, Julie covered environmental issues in the Great Lakes region for Michigan Radio’s “The Environment Report” and North Country Public Radio in New York. She’s won many awards, including an Edward R. Murrow Award in New York, and was named “Best Reporter” in Ohio by the Society of Professional Journalists. Her stories have aired on NPR’s “Morning Edition,” “The Splendid Table” and “Studio 360.” Julie loves covering agricultural issues for the Allegheny Front—exploring what we eat, who produces it and how it’s related to the natural environment. Prove your humanity
South Korea’s Hanwha Qcells has announced the launch of a new solar cell manufacturing facility in Cartersville, Georgia. This project marks the final stage of the massive Solar Hub industrial complex, which saw a $2.1 billion investment from the company. The new plant is of strategic importance for American solar energy, as it is the first complex to integrate the full cycle from silicon processing to the assembly of finished modules in one location. This is reported by Ixbt.com reports . Until now, most manufacturers in the USA were primarily engaged in final assembly using imported components. This model made the industry dependent on foreign suppliers, price fluctuations, and logistics issues. The Hanwha Qcells project serves to significantly reduce this dependency and strengthen the position of local manufacturing. Once the Cartersville facility reaches full capacity, it will be capable of producing up to 3.5 GW of solar modules annually. Currently, the assembly lines are operating in a standard mode, producing approximately 16,700 solar panels per day. This plant becomes part of the company’s unified production network in Georgia. Together with the expanded facility in Dalton, Hanwha Qcells’ total production capacity in the USA will reach 8.6 GW per year. According to the company’s estimates, this amount of equipment is sufficient to power approximately 1.3 million American households. The project also provides significant tax incentives under the US Inflation Reduction Act. Due to the full localization of production, Hanwha Qcells can receive subsidies for every stage, from silicon processing to the finished product. This, in turn, creates additional federal incentives for solar power plant projects that use local equipment. … «Zamin» – Ўзбекистон янгиликлари. Ўзбекистон матбуот ва ахборот агентлиги томонидан 1117-сонли гувоҳнома билан рўйхатдан ўтган. Электрон манзил: info@zamin.uz. Сайтда эълон қилинаётган муаллифлик мақолаларида билдирилган фикрлар муаллифга тегишли ва улар Zamin.uz таҳририяти нуқтаи назарини ифода этмаслиги мумкин.
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