Current Trends in the Development of Photovoltaic Forecasting Methods
DOI:
https://doi.org/10.24160/1993-6982-2025-6-98-105Keywords:
photovoltaic system, power prediction, machine learning methods, hybrid learning methods, artificial neural networkAbstract
In recent years, forecasting of the photovoltaic systems (PVS) power output has become a key aspect in power system management, especially in view of the growing use of renewable energy sources. Accurate PV power forecasting can help optimize grid operation, reduce costs, and improve grid stability. However, the high variability of power generation due to weather conditions and other external factors poses serious challenges for forecasting accuracy. The article presents a review of state-of-the-art PVS power forecasting methods, including conventionally used physical and statistical models, machine learning methods, and hybrid approaches. Special attention is paid to deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) models, which demonstrate high performance in handling complex nonlinear relationships and time series. Ensemble and hybrid methods that combine several approaches to improve the prediction accuracy are discussed. An important part of the study is an analysis of metrics for evaluating forecasting accuracy, such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The main sources of errors and factors affecting the forecasting results are also considered. The article outlines prospects for the development of forecasting methods, including trends toward the use of ensemble and hybrid models, as well as the need to standardize evaluation metrics for more accurately comparing different approaches. Thus, the presented review contributes to further development of forecasting methods and their application in smart grids.
References
1. Сергеев Н.Н., Матренин П.В. Обзор международного опыта в прогнозировании генерации возобновляемых источников энергии с помощью методов машинного обучения // iPolytech J. 2023. Т. 27. № 2. С. 354—369.
2. Salamanis A.I. e. a. Benchmark Comparison of Analytical, Data-based and Hybrid Models for Multi-step Short-term Photovoltaic Power Generation Forecasting // Energies. 2020. V. 13. P. 5978.
3. Farah S., Boland J. Time Series Model for Real-time Forecasting of Australian Photovoltaic Solar Farms Power Output. J. Renew. Sustain // Energy. 2021. V. 13. P. 046102.
4. Polimeni S., Nespoli A., Leva S., Valenti G., Manzolini G. Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results // Processes. 2021. V. 9. P. 323.
5. Камалова Д.М. и др. Обзор методов краткосрочного прогнозирования выработки электроэнергии фотоэлектрическими станциями // Международный журнал информационных технологий и энергоэффективности. 2024. Т. 9. № 7(45) С. 242—249.
6. Perera M., De Hoog J., Bandara K., Halgamuge S. Multi-resolution, Multi-horizon Distributed Solar PV Power Forecasting with Forecast Combinations // Expert Systems with Appl. 2022. V. 205. P. 117690.
7. Géron A. Hands-on Machine Learning with Scikit-learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Missouri: Southeast University Press, 2023.
8. Dimovski A., Moncecchi M., Falabretti D., Merlo M. PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: a Real-life Implementation on a Large-scale Pilot // Energies. 2020. V. 13. P. 5330.
9. Mubarak H. e. a. A Hybrid Machine Learning Method with Explicit Time Encoding for Improved Malaysian Photovoltaic Power Prediction // J. Cleaner Production. 2023. V. 382. P. 134979.
10. Матренин П.B. e. a. Повышение точности прогнозирования генерации фотоэлектрических станций на основе алгоритмов k-средних и k-ближайших соседей // Известия высших учеб. заведений и энергетических объединений СНГ. Серия «Энергетика». 2023. № 66(4). С. 305—321.
11. Goodfellow I., Bengio Y., Courville A. Deep Learning. N.-Y.: MIT Press, 2016.
12. Kumar P.M., Saravanakumar R., Karthick A., Mohanavel V. Artificial Neural Network-based Output Power Prediction of Grid-connected Semitransparent Photovoltaic System // Environmental Sci. and Pollution Research. 2022. V. 29. Pp. 10173—10182.
13. Qing X., Niu Y. Hourly Day-ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM // Energy. 2018. V. 148. Pp. 461—468.
14. Jiang J.R. e. a. Research on Photovoltaic Power Prediction Method Based on Deep Learning Algorithm // Automation Appl. 2023. V. 13. Pp. 102—104.
15. Dolara A. e. a. A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output // Energies. 2015. V. 8. Pp.1138—1153.
16. Zhang J. e. a. A Suite of Metrics for Assessing the Performance of Solar Power Forecasting // Solar Energy. 2015. V. 111. Pp. 157—175.
17. Raj V. e. a. Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems // Energies. 2023. V. 16(2). P. 671.
18. Воротынцев Д.В., Тягунов М.Г. Прогноз выработки электроэнергии фотоэлектрическими электростанциями (на сутки вперед) с использованием машинного обучения // Вестник МЭИ. 2018. № 4. С. 53—57.
19. Lauria D., Mottola F., Proto. D. Caputo Derivative Applied to Very Short Time Photovoltaic Power Forecasting // Appl. Energy. 2022. V. 309. P. 118452.
20. Mehazzem F., André M., Calif R. Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region // Energies. 2022. V. 15(22). P. 8671.
21. Ahn H.K., Park N. Deep RNN-Based Photovoltaic Power Short-term Forecast Using Power IoT Sensors // Energies. 2021. V. 14. P. 436.
22. Khortsriwong N. e. a. Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant // Energies. 2023. V. 16. P. 2119.
23. Tuyen N-D., Hieu D-D., Fujita G., Son T-T. Multi 2D-CNN-based Model for Short-term PV Power Forecast Embedded with Laplacian Attention // Energy Rep. 2024. V. 12. Pp. 2086—2096.
24. Ghimire S. e. a. Hybrid Convolutional Neural Network-multilayer Perceptron Model for Solar Radiation Prediction // Cognitive Computation. 2023. V. 15. Pp. 645—671.
25. Ju Y., Li J., Sun G. Ultra-short-term Photovoltaic Power Prediction Based on Self-attention Mechanism and Multi-task learning // IEEE Access. 2020. V. 8. Pp. 44821—44829.
26. Тюньков Д.А. и др. Нейросетевая модель для краткосрочного прогнозирования выработки электрической энергии солнечными электростанциями // Научный вестник НГТУ. 2020. № 4(80). С. 145—158.
27. Xie C.Z. e. a. Prediction of boa-GBDT Photovoltaic Output Based on Fine-grained Characteristics // J. Power System Technol. 2020. V. 44(2). Pp. 689—696.
28. Cordeiro-Costas M., Villanueva D., Eguía-Oller P., Granada-Álvarez E. Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting // Appl. Sci. 2022. V. 12. P. 8769.
29. Aljanad A., Tan N.M.L, Agelidis V.G., Shareef H. Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-time-intervals Using Particle Swarm Optimization Algorithm // Energies. 2021. V. 14(4). P. 1213.
30. Wang W., Wang B., Zhang J., Lu L., He X. New Energy Regional Power Prediction Algorithm Based on Statistical Upscaling in Ningbo Area // J. China Electric Power. 2020. V. 53(5). Pp. 100—111.
31. Zhu R., Li T., Tang B. Research on Short-term Photovoltaic Power Generation Forecasting Model Based on Multi-Strategy Improved Squirrel Search Algorithm and Support Vector Machine // Sci. Rep. 2024. V. 14. P. 14348.
32. Almaghrabi S., Rana M., Hamilton M., Rahaman M.S. Solar Power Time Series Forecasting Utilising Wavelet Coefficients // Neurocomputing. 2022. V. 508. Pp. 182—207.
33. Bozorg M. e. a. A Derivative-Persistence Method for Real Time Photovoltaic Power Forecasting // Proc. Intern. Conf. Smart Grids and Energy Systems. Perth, 2020. Pp. 843—847.
34. Bozorg M. e. a. Bayesian Bootstrapping in Real-time Probabilistic Photovoltaic Power Forecasting // Solar Energy. 2021. V. 225. Pp. 577—590.
35. Тюньков Д.А., Сапилова А.А., Грицай А.С., Алексеенко Д.А., Хамитов Р.Н. Методы краткосрочного прогнозивания выработки электрической энергии солнечными электростанциями и их классификация // Электротехнические системы и комплексы. 2020. № 3(48). С. 4—10.
36. Cui Y., Wu M., Jiang Y., Jing P. Short- and Medium-term Forecasting of Distributed PV Output in Plateau Regions Based on a Hybrid MLP-FGWO-PSO Approach // Energy Rep. 2024. V. 11. Pp. 2685—2691.
37. Wu Y., Xiang C., Qian H., Zhou P. Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm // Energies. 2024. V. 17(17). P. 4434.
---
Для цитирования: Чэнь Сяоюй, Ду Ян,Альмнхалави Хашим Али, Велькин В.И., Ли Цюаньпэн. Современные тенденции в области развития методов фотоэлектрического прогнозирования // Вестник МЭИ. 2025. № 6. С. 98—105. DOI: 10.24160/1993-6982-2025-6-98-105
---
Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Sergeev N.N., Matrenin P.V. Obzor Mezhdunarodnogo Opyta v Prognozirovanii Generatsii Vozobnovlyaemykh Istochnikov Energii s Pomoshch'yu Metodov Mashinnogo Obucheniya. iPolytech J. 2023;27;2:354—369. (in Russian).
2. Salamanis A.I. e. a. Benchmark Comparison of Analytical, Data-based and Hybrid Models for Multi-step Short-term Photovoltaic Power Generation Forecasting. Energies. 2020;13:5978.
3. Farah S., Boland J. Time Series Model for Real-time Forecasting of Australian Photovoltaic Solar Farms Power Output. J. Renew. Sustain. Energy. 2021;13:046102.
4. Polimeni S., Nespoli A., Leva S., Valenti G., Manzolini G. Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results. Processes. 2021;9:323.
5. Kamalova D.M. i dr. Obzor Metodov Kratkosrochnogo Prognozirovaniya Vyrabotki Elektroenergii Fotoelektricheskimi Stantsiyami. Mezhdunarodnyy Zhurnal Informatsionnykh Tekhnologiy i Energoeffektivnosti. 2024;9;7(45):242—249. (in Russian).
6. Perera M., De Hoog J., Bandara K., Halgamuge S. Multi-resolution, Multi-horizon Distributed Solar PV Power Forecasting with Forecast Combinations. Expert Systems with Appl. 2022;205:117690.
7. Géron A. Hands-on Machine Learning with Scikit-learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Missouri: Southeast University Press, 2023.
8. Dimovski A., Moncecchi M., Falabretti D., Merlo M. PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: a Real-life Implementation on a Large-scale Pilot. Energies. 2020;13:5330.
9. Mubarak H. e. a. A Hybrid Machine Learning Method with Explicit Time Encoding for Improved Malaysian Photovoltaic Power Prediction. J. Cleaner Production. 2023;382:134979.
10. Matrenin P.B. e. a. Povyshenie Tochnosti Prognozirovaniya Generatsii Fotoelektricheskikh Stantsiy na Osnove Algoritmov k-Srednikh i k-Blizhayshikh Sosedey. Izvestiya Vysshikh Ucheb. Zavedeniy i Energeticheskikh Ob'edineniy SNG. Seriya «Energetika». 2023;66(4):305—321. (in Russian).
11. Goodfellow I., Bengio Y., Courville A. Deep Learning. N.-Y.: MIT Press, 2016.
12. Kumar P.M., Saravanakumar R., Karthick A., Mohanavel V. Artificial Neural Network-based Output Power Prediction of Grid-connected Semitransparent Photovoltaic System. Environmental Sci. and Pollution Research. 2022;29:10173—10182.
13. Qing X., Niu Y. Hourly Day-ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM. Energy. 2018;148:461—468.
14. Jiang J.R. e. a. Research on Photovoltaic Power Prediction Method Based on Deep Learning Algorithm. Automation Appl. 2023;13:102—104.
15. Dolara A. e. a. A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output. Energies. 2015;8. Pp.1138—1153.
16. Zhang J. e. a. A Suite of Metrics for Assessing the Performance of Solar Power Forecasting. Solar Energy. 2015;111:157—175.
17. Raj V. e. a. Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems. Energies. 2023;16(2):671.
18. Vorotyntsev D.V., Tyagunov M.G. Prognoz Vyrabotki Elektroenergii Fotoelektricheskimi Elektrostantsiyami (na Sutki Vpered) s Ispol'zovaniem Mashinnogo Obucheniya. Vestnik MEI. 2018;4:53—57. (in Russian).
19. Lauria D., Mottola F., Proto. D. Caputo Derivative Applied to Very Short Time Photovoltaic Power Forecasting. Appl. Energy. 2022;309:118452.
20. Mehazzem F., André M., Calif R. Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region. Energies. 2022;15(22):8671.
21. Ahn H.K., Park N. Deep RNN-Based Photovoltaic Power Short-term Forecast Using Power IoT Sensors. Energies. 2021;14:436.
22. Khortsriwong N. e. a. Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant. Energies. 2023;16:2119.
23. Tuyen N-D., Hieu D-D., Fujita G., Son T-T. Multi 2D-CNN-based Model for Short-term PV Power Forecast Embedded with Laplacian Attention. Energy Rep. 2024;12:2086—2096.
24. Ghimire S. e. a. Hybrid Convolutional Neural Network-multilayer Perceptron Model for Solar Radiation Prediction. Cognitive Computation. 2023;15:645—671.
25. Ju Y., Li J., Sun G. Ultra-short-term Photovoltaic Power Prediction Based on Self-attention Mechanism and Multi-task learning. IEEE Access. 2020;8:44821—44829.
26. Tyun'kov D.A. i dr. Neyrosetevaya Model' dlya Kratkosrochnogo Prognozirovaniya Vyrabotki Elektricheskoy Energii Solnechnymi Elektrostantsiyami. Nauchnyy Vestnik NGTU. 2020;4(80):145—158. (in Russian).
27. Xie C.Z. e. a. Prediction of boa-GBDT Photovoltaic Output Based on Fine-grained Characteristics. J. Power System Technol. 2020;44(2):689—696.
28. Cordeiro-Costas M., Villanueva D., Eguía-Oller P., Granada-Álvarez E. Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting. Appl. Sci. 2022;12:8769.
29. Aljanad A., Tan N.M.L, Agelidis V.G., Shareef H. Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-time-intervals Using Particle Swarm Optimization Algorithm. Energies. 2021;14(4):1213.
30. Wang W., Wang B., Zhang J., Lu L., He X. New Energy Regional Power Prediction Algorithm Based on Statistical Upscaling in Ningbo Area. J. China Electric Power. 2020;53(5):100—111.
31. Zhu R., Li T., Tang B. Research on Short-term Photovoltaic Power Generation Forecasting Model Based on Multi-Strategy Improved Squirrel Search Algorithm and Support Vector Machine. Sci. Rep. 2024;14:14348.
32. Almaghrabi S., Rana M., Hamilton M., Rahaman M.S. Solar Power Time Series Forecasting Utilising Wavelet Coefficients. Neurocomputing. 2022;508:182—207.
33. Bozorg M. e. a. A Derivative-Persistence Method for Real Time Photovoltaic Power Forecasting. Proc. Intern. Conf. Smart Grids and Energy Systems. Perth, 2020:843—847.
34. Bozorg M. e. a. Bayesian Bootstrapping in Real-time Probabilistic Photovoltaic Power Forecasting. Solar Energy. 2021;225:577—590.
35. Tyun'kov D.A., Sapilova A.A., Gritsay A.S., Alekseenko D.A., Khamitov R.N. Metody Kratkosrochnogo Prognozivaniya Vyrabotki Elektricheskoy Energii Solnechnymi Elektrostantsiyami i Ikh Klassifikatsiya. Elektrotekhnicheskie Sistemy i Kompleksy. 2020;3(48):4—10. (in Russian).
36. Cui Y., Wu M., Jiang Y., Jing P. Short- and Medium-term Forecasting of Distributed PV Output in Plateau Regions Based on a Hybrid MLP-FGWO-PSO Approach. Energy Rep. 2024;11:2685—2691.
37. Wu Y., Xiang C., Qian H., Zhou P. Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm. Energies. 2024;17(17):4434
---
For citation: Chen Xiaoyu, Du Yang, Almnhalawi Hashim Ali, Velkin V.I., Li Quanpeng. Current Trends in the Development of Photovoltaic Forecasting Methods. Bulletin of MPEI. 2025;6:98—105. (in Russian). DOI: 10.24160/1993-6982-2025-6-98-105
---
Conflict of interests: the authors declare no conflict of interest
