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Scientific Reports volume 15, Article number: 14406 (2025) Cite this article
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With the steady annual growth of grid-connected photovoltaic (PV) power generation, the intermittent nature of this energy source has been increasingly drawing attention for its impact on grid stability. The output of photovoltaic power generation is highly influenced by weather factors and seasonal changes. The 24 solar terms are widely recognized as a reliable method for predicting weather conditions and seasonal shifts in China. Based on an analysis of the 24 solar terms, this work investigated their impact on PV power generation in China and established a correlation coefficient between PV output and solar terms. Subsequently, this paper proposed a grid connection method based on average values derived from the 24 solar terms and optimized it using a transfer learning model. The effectiveness of the proposed method was validated through a case study on a small-scale PV power station. It was validated that the proposed grid connection method increases power grid stability by 17.75%. The average grid connection method developed in this paper, which more accurately aligns with seasonal solar radiation variations, contributes to advancing grid-connected PV systems and provides a practical strategy for large-scale PV power integration.
As one of the cleanest energy sources, solar energy has drawn significant global attention for its development and utilization^1,2,3. A primary method of utilizing solar energy is its direct conversion into electricity through the photovoltaic (PV) effect in semiconductors, which can power buildings or be fed into the electrical grid^4,5,6,7. The output of solar PV power generation is affected by multiple factors, such as panel orientation, tilt angle, and weather variables, with some of these factors being responsible for the intermittent characteristics of PV power generation^8,9,10. When integrated into the electricity grid, the intermittent nature of PV power generation poses challenges to grid stability^11,12,13.
To minimize the adverse effects of PV power generation on the electricity grid, a significant portion of research has focused on predicting PV power generation, load forecasting, and power distribution and management. In reference¹⁴, a DSM method for maneuvering systems with different time scales considering schedulability and uncertainty was proposed, which can effectively realize the net load characteristics of the automatic governor and enhance the supply and demand balance of the power system. Reference¹⁵ proposed a demand flexibility strategy based on optimization, which adopted the characteristics of various mobile devices to reduce power costs and stabilize power fluctuations in the power grid. Reference¹⁶ decarbonized university campuses and adjacent communities by centralizing solar PV systems with energy storage systems and local power websites. Reference¹⁷ proposed an optimal scale algorithm for residential grid-connected PV cell systems, which minimized the total annual electricity bill. The results showed that jointly optimizing the scale of batteries and PV systems can significantly reduce electricity imports and household electricity costs.
Another branch of research emphasizes the optimization of grid-connected systems and the development of upgraded devices to address the challenges posed by PV intermittency to grid stability. Aiming at the problem of large power fluctuation and nonlinear inverter model in PV grid-connected system, an adaptive backstepping sliding mode controller with command filtering based on projection was designed to adjust the Direct Current (DC) voltage and Alternate Current(AC) current in PV grid-connected system¹⁸. The results showed that the control strategy can accurately control the grid-connected inverter. Reference¹⁹ adopted the reactive power capability of the voltage source inverter in the PV system to reduce the intermittent damage of the PV system to the power system. It showed that the designed reactive power control system can reduce the burden of the public grid control PV system. Reference²⁰ studied the grid frequency controller to reduce the current harmonic distortion generated by the PV power station. The results showed that the frequency controller can improve the performance of the PV power station under certain conditions. “Reference²¹ concentrated on the development of MPPT optimization algorithms to improve the quality of power in grid-connected systems.
In addition to technology, some policies and regulations have been considered to reduce the impact of PV power generation on the electricity grid. Many regions in China have proposed integrating PV power generation access volumes and access periods into the electricity grid, such as: Hebei Province has issued a notice that prohibits over-capacity access to photovoltaics, and regularly discloses the access capacity of the electricity grid²², Liaocheng City in Shandong Province has identified several counties and cities where the scale of PV development exceeds the regional load, requiring further development only after load increases²³, Due to the grid-connected PV capacity far surpassing the distributed power access capacity in Yingkou, Liaoning Province, the approval of new distributed power projects has been suspended²⁴.
In present, there are two problems for the PV system. The one is that weather-dependent power output leads to grid power fluctuations and the other one is temporal and spatial mismatches between power generation and load demand. Actually, load demand is also weather-dependent in many cases. Hence, the discussion about the influence of the weather on the PV power output has been important. Among the transient and massive weather datasets, choosing the reasonable weather-representative features parameters to predict the PV power output and investigate appropriate grid-connected strategies can play an important role on reducing grid fluctuations, which can reach the potential of existing PV systems and facilitate the expansion of future installations.
In view of the above problem, this paper proposed a grid connection method by average values based on 24 solar terms. Figure 1 illustrates the research framework. Firstly, the annual weather data for 2020 in certain representative regions were acquired from the National Renewable Energy Laboratory (NREL), and the PV power generation characteristic curves in the regions were simulated by System Advisor Model(SAM) software, so as to analyze the influence of 24 solar terms on PV power generation. Secondly, a method of periodic average grid connection was proposed. The 24 grid-connected cycles of one whole year were proposed according to three cases:1) the specific day of 24 solar terms, 2) three days before and after the specific day of 24 solar terms, and 3) the midway between two adjacent 24 solar terms. The results were obtained by comparing the grid-connected characteristics before and after the average grid-connected treatment in various regions: when connected to the grid adopting the case of the midway of two adjacent 24 solar terms, the fluctuation of the power grid reached the smallest. In contrast to prior works^{14,15,16,17,18,19,20} that optimize grid stability through reactive power control or demand-side management, this study proposes a calendar-driven approach leveraging the 24 solar terms. This method not only accounts for seasonal variations but also reduces dependency on complex real-time prediction models.
Frame diagram of a new method.
Current research indicates that voltage instability is one of the primary challenges in power systems with intermittent PV power generation^25,26,27. The peak and valley values of PV power generation have a significant impact on the stability of the power grid when incorporated into the grid. When PV power is integrated into the grid on a large scale, the grid voltage will increase rapidly at the peak of PV power generation, and will be off-grid at the valley of PV power generation, resulting in voltage instability and affecting voltage quality²⁸.
In this paper, the method of splitting node is used^29,30. Firstly, the annual PV power generation was divided into 24 research objects according to the distribution of 24 solar terms, and then the maximum(Max) and minimum(Min) values of PV power generation in each object were recorded respectively. Finally, the relationship between its distribution and solar terms was analyzed.
Solar radiation and ambient temperature, which change with the relative position between the Earth and the Sun, have a significant impact on PV power generation^31,32,33. As is known to all, the widely-used Gregorian calendar, which is based on the Moon’s orbit around the Earth, for PV power generation forecasting may not be an accurate indicator for the change of the season. Alternatively, the 24 solar terms originated from China was developed based on the sun’s position in the zodiac. The 24 solar terms divide the Sun’s annual circular motion into 24 equal segments, with each segment spanning 15° along the ecliptic. The 24 solar terms may be a more accurate indicator for PV power generation forecasting.
However, because the Sun’s speed along the ecliptic varies with the Earth-Sun distance, the number of days between two adjacent solar terms fluctuates slightly throughout the year, the durations of the 24 solar terms is not exactly equal³⁴. Table 1 lists the distribution dates of 24 solar terms in China in 2020.
The average annual solar radiation in Shandong Province in 2020 was 1518.90 kWh/m², which is similar to the national average annual radiation of 1490.80 kWh/m² (from: China Meteorological Administration). Therefore, this paper selects the meteorological data of Jinan City, Shandong Province in 2020 as an example to illustrate. The solar radiation, ambient temperature, and solar terms distribution are shown in Fig.2. It shows that there are some relevance between solar radiation and 24 solar terms.
The distribution of solar radiation and daily average temperature in 2020.
With the solar radiation, the paper uses the software SAM to simulate the PV power generation³⁵. SAM is an open source tool developed by the U.S. Department of Energy National Renewable Energy Laboratory. The software has been widely used for technical evaluation of renewable energy and hybrid energy systems. In this work, the SAM version 2022.11.21 was utilized with weather data from the NREL TMY3 files for Jinan, sampled at 5-min intervals.
Input parameters for the simulations include load demand, PV system and DC battery size, system components, weather data files at selected locations, etc. This study does not involve finance temporarily. For the purpose of comparison, this paper uses default financial parameters, system load parameters, system size, etc. to simulate. According to the latitude of the region³⁶, the installation angle of the PV panel is set, and the meteorological documents of the typical regions in 2020 are obtained through NREL to simulate the PV power generation.
Therefore, this paper selects the meteorological data of Jinan city as an example. Jinan City is located in eastern China, with a latitude of 36.64°N and a longitude of 117.13°E. The orientation and installation angle of the PV modules are shown in Fig. 3. The PV modules are facing the south and tilted by the local latitude. The simulation parameters of PV system are set as shown in Table 2.
Orientation and installation angle of PV modules.
In Table 2, Wdc represents DC power, Vdc represents DC voltage, Adc represents DC current, and kWdc represents DC capacity.
The relationship between the annual PV power generation and the distribution of 24 solar terms in Shandong in 2020 is shown in Fig. 4.
The annual PV power generation and the 24 solar terms in Shandong in 2020.
In this research, three cases are provided when the PV power generation is affected by the solar terms, namely Case I: the extreme value of PV power generation occurs on the specific day of 24 solar terms; Case II: The extreme value of PV power generation occurs three days before and after the specific day of 24 solar terms; Case III: The extreme value of PV power generation occurs midway between two adjacent 24 solar terms. Figure 5 shows the distribution of PV power generation and solar terms in Shandong Province in January 2020.
The distribution of PV power generation and solar terms in January 2020 in Shandong Province.
It can be seen from Fig. 5 that the minimum value of PV power generation in January occurs one day before the first solar term (Slight Cold), and the maximum value of PV power generation occurs in the middle of two adjacent solar terms (Slight Cold and Great Cold).
The degree of influence exerted by solar terms on PV power generation in each region can be considered as below:
where, (I{text{Total}}) represents the total percentage, (I_{Case}^{{text{I }}}) represents the percentage of the accumulative days of Case I, (I_{Case}^{{{text{II}}}}) represents the percentage of the accumulative days of Case II, (I_{{C{text{a}}se}}^{{{text{III}}}}) represents the percentage of the accumulative days of Case III.
The percentage of the accumulative days of three different cases can be calculated as below:
where, n represents the total number of all the maximum and minimum values, ({text{n}}_{max }^{i}) and ({text{n}}_{min }^{i}) represent the number of maximum and minimum values in the case i, respectively, where i = I, II,III.
The 24 solar terms partition a year into 24 distinct segments. According to the above extreme values and solar terms distribution statistics, each segment includes one maximum and one minimum values, then a total of 48 extreme values can be obtained. Among them, the days of Case I was 4, accounting for 8.33%. The days of Case II was 16, accounting for 33.33%. The days of Case III was 5, accounting for 10.42%. In summary, the total proportion of PV power generation in Shandong Province affected by solar terms was 52.83% , and the statistical results are shown in Fig. 6.
The proportion of PV power generation affected by solar terms in Shandong Province in 2020.
Table 3 provides a summary of the effects of solar terms on PV power generation in different regions of China in 2020. The three cases introduced account for 52.6% of the total number of days in a year, then 52.60% is set as the reference value. When the correlation value between PV power generation and solar terms is higher than the reference value, it is judged that PV power generation is affected by solar terms more significantly. If it is lower than the reference value, it shows that PV power generation in this area is affected by solar terms relatively slightly.
A total of 34 groups of PV power generation data affected by solar terms across different regions in China were analyzed. Figure 7 illustrates the influence of solar terms on PV power generation in various regions of China in 2020.
The distribution map of the influence of solar terms on PV power generation in different regions of China in 2020; (a) The correlation value more than 60%; (b) The correlation value less than 60%. Overlay calculated data by WPS 12.1.0.20305 on base maps sourced from the AutoNavi Open Platform. The URL is http://datav.aliyun.com/portal/school/atlas/area_selector.
Because the intermittence of PV power generation in most areas of China is related to the distribution of solar terms, the paper provides three average grid connection methods based on 24 solar terms, as shown in Fig. 8, where, Case1: The annual PV power generation is divided into 24 cycles according to 24 solar terms and each cycle accounts for 15 or 16 days; Case 2: Three days before and after the 24 solar terms are the average grid connection period; Case3: The grid connection capacity is based on the periodic average of the middle of the adjacent 24 solar terms.
Average grid-connected situation division diagram.
As shown in Fig.9, the comparisons before and after the treatment of the average value of PV power generation in Shandong in 2020 are shown as follows:
Comparison of PV power generation before and after treatment in Shandong Province in 2020.
In this paper, the variance (S^{2}) is introduced to measure the stability of grid-connected PV power generation^37,38,39. Variance reduction was calculated using Python’s SciPy library (v1.10.1) with a 95% confidence interval. The smaller the variance, the more stable the group of data, the greater the variance, the more unstable the group of data. The variance can be calculated as below:
In Eq. (3), ({text{n}}year) represents the number of days in a year, (overline{{text{x}}}) represents the annual average value of PV power generation grid-connected electricity, ({text{x}}i) represents the PV power generation grid-connected electricity on the same day. The equation of (overline{{text{x}}}) is :
For Case 1, the variance is 3.52.
For Case 2, the average method contain 6 cycles, denoted as CaseB1, CaseB2, CaseB3, CaseA1, CaseA2, CaseA3. Considering the average of 6 cycles, as shown in Fig. 10, the average variance is 3.79.
The subdivision diagram of the grid connected with the periodic average value of the three days before and after the 24 solar terms.
For Case 3, the variance is 3.57.
According to the variances in Fig. 9, it can be seen that the proposed three average grid-connected methods can reduce the impact of PV power generation on the grid. Among them, the average grid-connected method with the middle of 24 solar terms has the smallest impact on the grid, with an impact reduction of 42.39%. The variances before and after PV grid-connected processing in various regions in China were analyzed and summarized in the same way, as shown in Table 4.
Table 4 shows that the variances of the three average grid connection methods are similar. However, the comparison reveals that in most regions, Case 3 exhibits the smallest variance, indicating the least fluctuation.
When the above average grid connection method is adopted, a battery is required to manage the power shift. To optimize battery capacity and enhance system economic efficiency, this paper proposes a new average grid-connected method based on the above research, which is based on the transfer learning model^40,41,42. By learning the grid-connected power generation data in one year, the grid-connected power is set in advance for the new grid-connected system. At this time, the grid-connected mode is divided into two types: (a) when the PV power generation is lower than or equal to the set value, all the generated power is incorporated into the grid; (b) when the day ‘s PV power generation is higher than the set value, the set power is incorporated into the grid, and the excess power is stored. At the same time, the stored power is divided into the remaining days of the current cycle, and the set value of grid-connected power is updated. The new set value is :
where, (x_{i}^{n}) represents the updated grid-connected power, (y_{ij}) represents the actual PV power generation, (x_{i}^{{}}) represents the set grid-connected power,(mu) represents the remaining days in the corresponding solar term cycle,(_{i}) represents each cycle,(_{j}) represents the number of days in each cycle. Figure 11 illustrates the flowcharts of the prediction model used in this study.
Diagram of new average grid-connected prediction model.
The distributed PV grid-connected power stations located in Liaocheng City, Shandong Province were selected as the research objects. The parameters of the power station equipment are shown in Table 5.
The daily PV power generation and grid-connected power from January 2020 to December 2020 were collected, as shown in Fig. 12(a). According to the average grid-connected method Case 3 described in Section “Average grid connection method based on 24 solar terms“, the daily grid-connected power was shown in Fig. 12(b).
Comparison of grid-connected power before and after average treatment in 2020; (a) The original grid-connected power; (b) The grid-connected power after average grid-connected processing with Case 3;
The variance of original grid-connected power before average processing is 327.77, and the variance of grid-connected power after average processing is 101.13. The stability of power grid increases by 69.15%.
When the new average grid connection method described in Section “Transfer learning model based on average grid connection method ” was implemented, the grid-connected power is shown in Fig. 13, with a variance of 269.58, a 17.75% improvement in grid stability.
Profile of grid-connected power quantity by new average grid-connected method.
By analyzing the influence of solar terms on PV power generation in various regions in China, the method of average grid connection based on 24 solar terms is proposed and optimized by transfer learning model , and the variances of the average grid-connected power generation are compared with that of the existing PV power generation grid-connected system, which proves that the proposed method can improve the stability of the power grid.
The main conclusions and discussion of this paper are:
A novel grid connection method based on China’s 24 solar terms is proposed, which align more precisely with seasonal solar radiation variations, and uniquely integrates traditional meteorological indicators with modern transfer learning model for dynamic grid management.
The PV power generation in most regions of China is influenced by solar terms. Among these regions, areas between the Yangtze River Basin and the Yellow River Basin (such as Henan, Shanxi, and Shaanxi) show particularly noticeable effects from solar terms on their PV generation. In contrast, coastal and northern regions (including Shanghai, Jiangsu, and Fujian) demonstrate relatively weaker impacts from solar terms.
For the countries and regions not adopting solar terms, suitable characteristic values of the local meteorological date can be used for the method of average grid connection according to the research method introduced in this paper.
When the proposed average grid-connected method is considered, there are many other factors, such as the user ‘s self-consumption of power, investment payback period, etc., which need to be further discussed in the coming works.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Ertugrul, N. & Abbott, D. DC is the future [Point of View]. Proc. IEEE 108(5), 615–624. https://doi.org/10.1109/JPROC.2020.2982707 (2020).
Article  Google Scholar 
Boruah, D. & Chandel, S. S. A comprehensive analysis of eight rooftop grid-connected solar photovoltaic power plants with battery energy storage for enhanced energy security and grid resiliency[J]. Sol. Energy 266, 112154. https://doi.org/10.1016/j.solener.2023.112154 (2023).
Article  Google Scholar 
Wang, H., Mao, L., Zhang, H. & Wu, Q. Multi-prediction of electric load and photovoltaic solar power in grid-connected photovoltaic system using state transition method[J]. Appl. Energy 353, 122138. https://doi.org/10.1016/j.apenergy.2023.122138 (2024).
Article  Google Scholar 
Rout, K. C. Design of grid-connected rooftop photovoltaic system for leakage current reduction using optimization algorithms[J]. Sol. Energy 263, 111832. https://doi.org/10.1016/j.solener.2023.111832 (2023).
Article  Google Scholar 
Attia, A. M. et al. A multi-objective optimization model for sizing decisions of a grid-connected photovoltaic system[J]. Energy 229, 120730. https://doi.org/10.1016/j.energy.2021.120730 (2021).
Article  Google Scholar 
Bey, M., Hamidat, A. & Nacer, T. Eco-energetic feasibility study of using grid-connected photovoltaic system in wastewater treatment plant[J]. Energy 216, 119217. https://doi.org/10.1016/j.energy.2020.119217 (2021).
Article  Google Scholar 
Khelil, C. K. M. et al. New intelligent fault diagnosis (IFD) approach for grid-connected photovoltaic systems[J]. Energy 211, 118591. https://doi.org/10.1016/j.energy.2020.118591 (2020).
Article  Google Scholar 
Savas A , Yavuz A . Pilot Scheme Conceptual Analysis of Rooftop East–West-Oriented Solar Energy System with Optimizer[J]. Energies, 2023, 16(5): 2396–2396. https://www.mdpi.com/1996-1073/16/5/2396
Habib S , Tamoor M , Gulzar M M , et al. Design, techno-economic evaluation, and experimental testing of grid connected rooftop solar photovoltaic systems for commercial buildings[J]. Frontiers in Energy Research, 2024, 121483755–1483755. https://www.frontiersin.org/journals/energy-research/articles/https://doi.org/10.3389/fenrg.2024.1483755/full
ATEŞ Y, Gökçek T, ARABUL A Y. Impact of hybrid power generation on voltage, losses, and electricity cost indistribution networks[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2021, 29(3): 1720–1735. https://journals.tubitak.gov.tr/elektrik/vol29/iss3/26/
Gao, F., Hu, R. & Yin, L. Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems[J]. Energy 264, 126278. https://doi.org/10.1016/j.energy.2022.126278 (2023).
Article  Google Scholar 
Naderipour, A. et al. Optimal design of hybrid grid-connected photovoltaic/wind/battery sustainable energy system improving reliability, cost and emission[J]. Energy 257, 124679. https://doi.org/10.1016/j.energy.2022.124679 (2022).
Article  Google Scholar 
Zhang, Y., Zhao, X., Wang, X., Li, A. & Wu, X. Multi-objective optimization design of a grid-connected hybrid hydro-photovoltaic system considering power transmission capacity[J]. Energy 284, 128683. https://doi.org/10.1016/j.energy.2023.128683 (2023).
Article  Google Scholar 
Yang, X., Zhang, Y., He, H., Ren, S. & Weng, G. Real-time demand side management for a microgrid considering uncertainties[J]. IEEE Trans. Smart Grid 10(3), 3401–3414. https://doi.org/10.1109/TSG.2018.2825388 (2018).
Article  ADS  Google Scholar 
Wang, C., Ji, J. & Yang, H. Day-ahead schedule optimization of household appliances for demand flexibility: Case study on PV/T powered buildings[J]. Energy 289, 130042. https://doi.org/10.1016/j.energy.2023.130042 (2024).
Article  Google Scholar 
Gul, E. et al. A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing[J]. Energy 244, 123191. https://doi.org/10.1016/j.energy.2022.123191 (2022).
Article  CAS  Google Scholar 
Li, J. Optimal sizing of grid-connected photovoltaic battery systems for residential houses in Australia[J]. Renew. Energy 136, 1245–1254. https://doi.org/10.1016/j.renene.2018.09.099 (2019).
Article  Google Scholar 
Xu, D., Wang, G., Yan, W. & Yan, X. A novel adaptive command-filtered backstepping sliding mode control for PV grid-connected system with energy storage[J]. Sol. Energy 178, 222–230. https://doi.org/10.1016/j.solener.2018.12.033 (2019).
Article  ADS  Google Scholar 
Kolhe, M. L. & Rasul, M. 3-Phase grid-connected building integrated photovoltaic system with reactive power control capability[J]. Renew. Energy 154, 1065–1075. https://doi.org/10.1016/j.renene.2020.03.075 (2020).
Article  Google Scholar 
Mirhosseini, M. Sensitivity analysis, adaptability improvement and control of grid-connected photovoltaic power plants under grid frequency variations[J]. Sol. Energy 184, 260–272. https://doi.org/10.1016/j.solener.2019.03.072 (2019).
Article  ADS  Google Scholar 
Prajapati S , Garg R , Mahajan P . Novel adaptive MPPT technique for enhanced performance of grid integrated solar photovoltaic system[J]. Computers and Electrical Engineering, 2024, 120(PA): 109648–109648. https://www.sciencedirect.com/science/article/abs/pii/S0045790624005755
Hebei province reform and development committee. Notice on strengthening the management of roof distributed photovoltaic power generation: Hebei Development and Reform Energy [2022] No.1243[A/OL].(2022–09–27)[2024–05–11]. http://hbdrc.hebei.gov.cn/xxgk_2232/zc/wzcwj/202309/t20230907_92136.html
Liaocheng Development and Reform Commission. Liaocheng Distributed Photovoltaic Development Special Plan (2023–2035) (Draft) [EB/OL]. (2023–06–08) [2024–05–11]. http://fgw.liaocheng.gov.cn/channel_t_165_12299/doc_648c7821b41c401e6622ef55.html
Yingkou Development and Reform Commission. Notice on the work related to the filing of distributed photovoltaic projects: ying power generation policy [2023] 105 [A/OL]. (2023–07–26) [2024–05–11]. https://www.ln.gov.cn/web/zwgkx/lnsrmzfgb/2024n/qk/2024n_dbq/bmwj/2024041913582452778/index.shtml
da Silva, B. R., Zilles, R. & Pinho, J. T. Overcoming the power factor apparent degradation of loads fed by photovoltaic distributed generators[J]. Renew. Energy 164, 1364–1375. https://doi.org/10.1016/j.renene.2020.10.146 (2021).
Article  Google Scholar 
Modarresi, J., Gholipour, E. & Khodabakhshian, A. A comprehensive review of the voltage stability indices[J]. Renew. Sustain. Energy Rev. 63, 1–12. https://doi.org/10.1016/j.rser.2016.05.010 (2016).
Article  Google Scholar 
Rahman, S. et al. A framework to assess voltage stability of power grids with high penetration of solar PV systems[J]. Int. J. Electr. Power Energy Syst. 139, 107815. https://doi.org/10.1016/j.ijepes.2021.107815 (2022).
Article  Google Scholar 
Gandhi, O., Kumar, D. S., Rodriguez-Gallegos, C. D. & Srinivasan, D. Review of power system impacts at high PV penetration Part I: Factors limiting PV penetration[J]. Sol. Energy 210, 181–201. https://doi.org/10.1016/j.solener.2020.06.097 (2020).
Article  ADS  Google Scholar 
Yang, K., Yang, J. & Tian, X. Learning multi-granularity features from multi-granularity regions for person re-identification[J]. Neurocomputing 432, 206–215. https://doi.org/10.1016/j.neucom.2020.12.016 (2021).
Article  Google Scholar 
Wang, H. et al. Image super-resolution using multi-granularity perception and pyramid attention networks[J]. Neurocomputing 443, 247–261. https://doi.org/10.1016/j.neucom.2021.03.010 (2021).
Article  Google Scholar 
Jo, H. H., Kim, J. & Kim, S. Enhancing the power generation performance of photovoltaic system: Impact of environmental and system factors[J]. Appl. Therm. Eng. 240, 122221. https://doi.org/10.1016/j.applthermaleng.2023.122221 (2024).
Article  Google Scholar 
Jathar, L. D. et al. Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle[J]. Environ. Pollut. 326, 121474. https://doi.org/10.1016/j.envpol.2023.121474 (2023).
Article  CAS  PubMed  Google Scholar 
Ganti, P. K., Naik, H. & Barada, M. K. Environmental impact analysis and enhancement of factors affecting the photovoltaic (PV) energy utilization in mining industry by sparrow search optimization based gradient boosting decision tree approach[J]. Energy 244, 122561. https://doi.org/10.1016/j.energy.2021.122561 (2022).
Article  Google Scholar 
Xie, J. & Hong, T. Load forecasting using 24 solar terms[J]. J. Mod. Power Syst. Clean Energy 6(2), 208–214. https://doi.org/10.1007/s40565-017-0374-0 (2018).
Article  Google Scholar 
SAM Intro: Blair, Nate, Dobos, Aron P., Freeman, Janine, Neises, Ty, Wagner, Michael, Ferguson, Tom, Gilman, Paul, and Janzou, Steven. System Advisor model. United States: N: General Description; General Description. United States: Web. https://doi.org/10.2172/1126294 (2014).
Fouad, M. M., Shihata, L. A. & Morgan, E. S. I. An integrated review of factors influencing the performance of photovoltaic panels[J]. Renew. Sustain. Energy Rev. 80, 1499–1511. https://doi.org/10.1016/j.rser.2017.05.141 (2017).
Article  Google Scholar 
Elmousalami, H., Elshaboury, N. & Elyamany, A. H. Green artificial intelligence for cost-duration variance prediction (CDVP) for irrigation canals rehabilitation projects[J]. Expert Syst. Appl. 249, 123789. https://doi.org/10.1016/j.eswa.2024.123789 (2024).
Article  Google Scholar 
Kim, D. & Oh, M. Dynamic realized minimum variance portfolio models[J]. J. Bus. Econ. Stats. (just-accepted) https://doi.org/10.1080/07350015.2024.2308106 (2024).
Article  Google Scholar 
Fernandes, R. et al. Evidence of a bias-variance trade off when correcting for bias in Sentinel 2 forest LAI retrievals using radiative transfer models[J]. Remote Sens. Environ. 305, 114060. https://doi.org/10.1016/j.rse.2024.114060 (2024).
Article  Google Scholar 
Weiss, K., Khoshgoftaar, T. M. & Wang, D. D. A survey of transfer learning[J]. J. Big data 3(1), 1–40. https://doi.org/10.1186/s40537-016-0043-6 (2016).
Article  Google Scholar 
Chen, Y. et al. Transfer learning and pretraining enhanced physics-informed machine learning for closed-circuit reverse osmosis modeling[J]. Desalination 580, 117557. https://doi.org/10.1016/j.desal.2024.117557 (2024).
Article  CAS  Google Scholar 
Liu, F. et al. Transfer learning-based encoder-decoder model with visual explanations for infrastructure crack segmentation: New open database and comprehensive evaluation[J]. Undergr. Space 17, 60–81. https://doi.org/10.1016/j.undsp.2023.09.012 (2024).
Article  Google Scholar 
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This research was supported by Shandong Provincial Natural Science Foundation (No. ZR2021ME051 and ZR2023ME168) and Qingdao Agricultural University Doctoral Start-Up Fund (No. 1123016).
Natural Science Foundation of Shandong Province,ZR2021ME051,ZR2023ME168,Qingdao Agricultural University Doctoral Start-Up Fund,1123016
College of Civil Engineering and Architecture, Qingdao Agricultural University, Qingdao, 266109, People’s Republic of China
Qingqing Li, Wangjie Pan, Wangwang Jin, Qian Li, Zede Liang & Yuan Li
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QQ.L.: Conceptualization, Methodology and review, W.P.: Methodology, Software and Writing, W.J. and Q.L.: Data curation, Writing, Z.L. and Y.L.: Software, Validation. All authors reviewed the manuscript.
Correspondence to Qingqing Li or Yuan Li.
The authors declare no competing interests.
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Li, Q., Pan, W., Jin, W. et al. A new method to improve the power quality of photovoltaic power generation based on 24 solar terms. Sci Rep 15, 14406 (2025). https://doi.org/10.1038/s41598-025-97736-3
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Received: 18 September 2024
Accepted: 07 April 2025
Published: 24 April 2025
Version of record: 24 April 2025
DOI: https://doi.org/10.1038/s41598-025-97736-3
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