Обзор методов прогнозирования мощности фотоэлектрических систем
Ключевые слова:
фотоэлектрическая система, прогнозирование мощности, временные масштабы прогнозирования, пространственные масштабы прогнозирования, входные параметры
Аннотация
Комплексный временной и пространственный анализ прогнозирования мощности фотоэлектрических систем играет ключевую роль в обеспечении стабильной работы энергосистемы и эффективного потребления возобновляемой энергии. Рассмотрены существующие исследования методов прогнозирования мощности фотоэлектрических систем с точки зрения различных временных и пространственных масштабов.
Методы прогнозирования классифицированы в соответствии с процессом прогнозирования, временными и пространственными масштабами. На основе временных и пространственных масштабов производства фотоэлектрической энергии систематизированы методы прогнозирования в различных временных масштабах (ультракороткосрочных, краткосрочных и средне- и долгосрочных) и пространственных масштабах (для одной станции, региона), а также сценарии их применения, и изучены технические проблемы. Предложены будущие направления развития, такие как разработка упрощенных моделей, совместное прогнозирование в разных масштабах и гибридные методы, сочетающие физическое моделирование и анализ данных.
Настоящая работа представляет собой теоретическую основу для выбора и оптимизации методов прогнозирования фотоэлектрической мощности, помогает интеллектуальному управлению электрическими сетями и широкомасштабному применению возобновляемых источников энергии, а также закладывает основу для дальнейших исследований современных методов прогнозирования.
Литература
1. Kuo W.-C., Chen C.-H., Hua S.-H., Wang C.-C. Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System // Appl. Sci. 2022. V. 12(15). P. 7529.
2. Cui H., Li B.I. Research on Photovoltaic Power Forecasting Model Based on Hybrid Neural Network // Power System Protection and Control. 2021. V. 49(13). Pp. 142—149.
3. Wang. H. e. a. Global Sensitivity Analysis for Islanded Microgrid Based on Sparse Polynomial Chaos Expansion // Automation of Electric Power Systems. 2019. V. 43(10). Pp. 44—57.
4. Liu Q. e. a. Deep Learning Photovoltaic Power Generation Model Based on Time Series // Power System Protection and Control. 2021. V. 49(19). Pp. 87—98.
5. Ji X. e. a. Short-term Photovoltaic Power Forecasting Based on MIE-LSTM // Power System Protection and Control. 2020. V. 48(7). Pp. 50—57.
6. Bozorg M. e. a. Bayesian Bootstrap Quantile Regression for Probabilistic Photovoltaic Power Forecasting // Protection and Control of Modern Power Systems. 2020. V. 5. P. 21.
7. Jakoplić A., Franković D., Havelka J., Bulat H. Short-term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning // Energies. 2023. V. 16(14). P. 5428.
8. Stüber M. e. a. Forecast Quality of Physics-based and Data-driven PV Performance Models for a Small-scale PV System // Frontiers Energy Research. 2021. V. 9. P. 639346.
9. Duman Altan A., Diken B., Kayisoglu B. Prediction of Photovoltaic Panel Power Outputs Using Time Series and Artificial Neural Network Methods // J. Tekirdag Agricultural Faculty. 2021. V. 18. Pp. 457—469.
10. Farah S., Boland J. Time Series Model For Real-time Forecasting of Australian Photovoltaic Solar Farms Power Output // J. Renewable and Sustainable Energy. 2021. V. 13(4). P. 046102.
11. Saint-Drenan Y-M. e. a. Bayesian Parameterisation of a Regional Photovoltaic Model — Application to Forecasting // Solar Energy. 2019. V. 188. Pp. 760—774.
12. Jogunuri S. e. a. Random Forest Machine Learning Algorithm Based Seasonal Multi-step Ahead Short-term Solar Photovoltaic Power Output Forecasting // IET Renewable Power Generation. 2024. V. 19(1). Pp. 1—16.
13. Venugopal V., Sun Y., Brandt A.R. Short-term Solar PV Forecasting Using Computer Vision: The Search for Optimal CNN Architectures for Incorporating Sky Images and PV Generation History // J. Renewable and Sustainable Energy. 2019. V. 11(6). P. 066102.
14. Wai R-J., Lai P-X. Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data // Energies. 2022. V. 15(10). P. 3838.
15. Almaghrabi S., Rana M., Hamilton M., Rahaman M.S. Spatially Aggregated Photovoltaic Power Prediction Using Wavelet and Convolutional Neural Networks // Proc. Intern. Joint Conf. Neural Networks (IJCNN). Shenzhen, 2021. Pp. 1—8.
16. Wang Y., Wang X., Chen W., Yan K. Short-term Photovoltaic Output Forecasting Method Using LSTM Neural Network Considering Spatial Correlation // J. Automation Power System. 2020. V. 32. Pp. 78—85.
17. Зацаринная Ю.Н. и др. Прогнозирование выработки электроэнергии от ВИЭ методами машинного обучения // Известия высших учебных заведений. Серия «Проблемы энергетики». 2023. № 25(3). С. 81—92.
18. Wang S. e. a. A New Hybrid Short-term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM // Energies. 2020. V. 13(1). P. 87.
19. Құттыбай Н.Б., Айтбекова Ш.Б., Қошқарбай Н.Ж., Болатбек А.Б., Жоламанов Б.Н. Прогнозирование выходной мощности фотоэлектрической системына основе нейронных сетей // Информационно-коммуникационные и химические технологии. 2024. № 1(22). С. 84—96.
20. Chen Z.B. e. a. Photovoltaic Power Ultra Short-term Prediction Model Based on Ground Cloud Image and Radial Basis Function Artificial Neural Network // Proc. Chinese Soc. Electrical and Electronics Eng. Conf. 2015. V. 35. Pp. 561—567.
21. Ozoegwu C.G. Artificial Neural Network Forecast of Monthly Mean Daily Global Solar Radiation of Selected Locations Based on Time Series and Month Number // J. Cleaner Production. 2019. V. 216. Pp. 1—13.
22. Heo J., Jung J., Kim B., Han S. Digital Elevation Model-based Convolutional Neural Network Modeling for Searching of High Solar Energy Regions // Appl. Energy. 2020. V. 262. P. 114588.
23. Aslam M., Lee J-M., Kim H-S., Lee S-J., Hong S. Deep Learning Models for Long-term Solar Radiation Forecasting Considering Microgrid Installation: a Comparative Study // Energies. 2020. V. 13(1). P. 147.
24. Jung Y., Jung J., Kim B., Han S. Long Short-term Memory Recurrent Neural Network for Modeling Temporal Patterns in Long-term Power Forecasting for Solar PV Facilities: Case Study of South Korea // J. Cleaner Production. 2020. V. 250. P. 119476.
25. 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.
26. 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.
27. Моргоева А.Д., Моргоев И.Д., Клюев Р.В., Кочковская С.С. Прогнозирование почасовой выработки электроэнергии солнечной электростанцией с помощью алгоритмов машинного обучения // Известия Томского политехн. ун-та. Серия «Инжиниринг георесурсов». 2023. № 334(12). С. 7—19.
28. Kumari P., Toshniwal D. Long Short Term Memory-сonvolutional Neural Network Based Deep Hybrid Approach for Solar Irradiance Forecasting // Appl. Energy. 2021. V. 295. P. 117061.
29. Basaran K., Çelikten A., Bulut H. A Short-term Photovoltaic Output Power Forecasting Based on Ensemble Algorithms Using Hyperparameter Optimization // Electrical Eng. 2024. V. 106. Pp. 5319—5337.
30. Hu K. e. a. Short-term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer // Sustainability. 2024. V. 16(14). P. 5940.
31. 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.
32. Eom H., Son Y., Choi S. Feature-selective Ensemble Learning-based Long-term Regional PV Generation Forecasting // IEEE Access. 2020. V. 8. Pp. 54620—54630.
33. Jiao L. e. a. Spatial Clustering Method for Large-scale Distributed User Photovoltaics Based on Spatial Correlation // J. Automation Power System. 2019. V. 43. Pp. 97—102.
34. Goh S.M. e. a. Hardware Implementation of an Active Learning Self-organizing Neural Network to Predict the Power Fluctuation Events of a Photovoltaic Grid-tied System // Microprocessors and Microsystems. 2022. V. 90. P. 104448
---
Для цитирования: Чэнь Сяоюй, Ду Ян, Альмнхалави Хашим Али, Велькин В.И., Ли Цюаньпэн. Обзор методов прогнозирования мощности фотоэлектрических систем // Вестник МЭИ. 2025. № 5. С. 41—50. DOI: 10.24160/1993-6982-2025-5-41-50
---
Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Kuo W.-C., Chen C.-H., Hua S.-H., Wang C.-C. Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System. Appl. Sci. 2022;12(15):7529.
2. Cui H., Li B.I. Research on Photovoltaic Power Forecasting Model Based on Hybrid Neural Network. Power System Protection and Control. 2021;49(13):142—149.
3. Wang. H. e. a. Global Sensitivity Analysis for Islanded Microgrid Based on Sparse Polynomial Chaos Expansion. Automation of Electric Power Systems. 2019;43(10):44—57.
4. Liu Q. e. a. Deep Learning Photovoltaic Power Generation Model Based on Time Series. Power System Protection and Control. 2021;49(19):87—98.
5. Ji X. e. a. Short-term Photovoltaic Power Forecasting Based on MIE-LSTM. Power System Protection and Control. 2020;48(7):50—57.
6. Bozorg M. e. a. Bayesian Bootstrap Quantile Regression for Probabilistic Photovoltaic Power Forecasting. Protection and Control of Modern Power Systems. 2020;5:21.
7. Jakoplić A., Franković D., Havelka J., Bulat H. Short-term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning. Energies. 2023;16(14):5428.
8. Stüber M. e. a. Forecast Quality of Physics-based and Data-driven PV Performance Models for a Small-scale PV System. Frontiers Energy Research. 2021;9:639346.
9. Duman Altan A., Diken B., Kayisoglu B. Prediction of Photovoltaic Panel Power Outputs Using Time Series and Artificial Neural Network Methods. J. Tekirdag Agricultural Faculty. 2021;18:457—469.
10. Farah S., Boland J. Time Series Model For Real-time Forecasting of Australian Photovoltaic Solar Farms Power Output. J. Renewable and Sustainable Energy. 2021;13(4):046102.
11. Saint-Drenan Y-M. e. a. Bayesian Parameterisation of a Regional Photovoltaic Model — Application to Forecasting. Solar Energy. 2019;188:760—774.
12. Jogunuri S. e. a. Random Forest Machine Learning Algorithm Based Seasonal Multi-step Ahead Short-term Solar Photovoltaic Power Output Forecasting. IET Renewable Power Generation. 2024;19(1):1—16.
13. Venugopal V., Sun Y., Brandt A.R. Short-term Solar PV Forecasting Using Computer Vision: The Search for Optimal CNN Architectures for Incorporating Sky Images and PV Generation History. J. Renewable and Sustainable Energy. 2019;11(6):066102.
14. Wai R-J., Lai P-X. Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data. Energies. 2022;15(10):3838.
15. Almaghrabi S., Rana M., Hamilton M., Rahaman M.S. Spatially Aggregated Photovoltaic Power Prediction Using Wavelet and Convolutional Neural Networks. Proc. Intern. Joint Conf. Neural Networks (IJCNN). Shenzhen, 2021:1—8.
16. Wang Y., Wang X., Chen W., Yan K. Short-term Photovoltaic Output Forecasting Method Using LSTM Neural Network Considering Spatial Correlation. J. Automation Power System. 2020;32:78—85.
17. Zacarinnaya Yu.N. i dr. Prognozirovanie Vyrabotki Elektroenergii ot VIE Metodami Mashinnogo Obucheniya. Izvestiya Vysshikh Uchebnykh Zavedeniy. Seriya «Problemy Energetiki». 2023;25(3):S. 81—92. (in Russian).
18. Wang S. e. a. A New Hybrid Short-term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM. Energies. 2020;13(1):87.
19. Құttybay N.B., Aytbekova Sh.B., Қoshқarbay N.Zh., Bolatbek A.B., Zholamanov B.N. Prognozirovanie Vykhodnoy Moshchnosti Fotoelektricheskoy Sistemyna Osnove Neyronnykh Setey. Informacionno-kommunikacionnye I Khimicheskie Tekhnologii. 2024;1(22):84—96. (in Russian).
20. Chen Z.B. e. a. Photovoltaic Power Ultra Short-term Prediction Model Based on Ground Cloud Image and Radial Basis Function Artificial Neural Network. Proc. Chinese Soc. Electrical and Electronics Eng. Conf. 2015;35:561—567.
21. Ozoegwu C.G. Artificial Neural Network Forecast of Monthly Mean Daily Global Solar Radiation of Selected Locations Based on Time Series and Month Number. J. Cleaner Production. 2019;216:1—13.
22. Heo J., Jung J., Kim B., Han S. Digital Elevation Model-based Convolutional Neural Network Modeling for Searching of High Solar Energy Regions. Appl. Energy. 2020;262:114588.
23. Aslam M., Lee J-M., Kim H-S., Lee S-J., Hong S. Deep Learning Models for Long-term Solar Radiation Forecasting Considering Microgrid Installation: a Comparative Study. Energies. 2020;13(1):147.
24. Jung Y., Jung J., Kim B., Han S. Long Short-term Memory Recurrent Neural Network for Modeling Temporal Patterns in Long-term Power Forecasting for Solar PV Facilities: Case Study of South Korea. J. Cleaner Production. 2020;250:119476.
25. 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.
26. 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.
27. Morgoeva A.D., Morgoev I.D., Klyuev R.V., Kochkovskaya S.S. Prognozirovanie Pochasovoy Vyrabotki Elektroenergii Solnechnoy Elektrostanciey s Pomoshch'yu Algoritmov Mashinnogo Obucheniya. Izvestiya Tomskogo Politekhn. Un-ta. Seriya «Inzhiniring Georesursov». 2023;334(12):7—19. (in Russian).
28. Kumari P., Toshniwal D. Long Short Term Memory-сonvolutional Neural Network Based Deep Hybrid Approach for Solar Irradiance Forecasting. Appl. Energy. 2021;295:117061.
29. Basaran K., Çelikten A., Bulut H. A Short-term Photovoltaic Output Power Forecasting Based on Ensemble Algorithms Using Hyperparameter Optimization. Electrical Eng. 2024;106:5319—5337.
30. Hu K. e. a. Short-term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer. Sustainability. 2024;16(14):5940.
31. 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.
32. Eom H., Son Y., Choi S. Feature-selective Ensemble Learning-based Long-term Regional PV Generation Forecasting. IEEE Access. 2020;8:54620—54630.
33. Jiao L. e. a. Spatial Clustering Method for Large-scale Distributed User Photovoltaics Based on Spatial Correlation. J. Automation Power System. 2019;43:97—102.
34. Goh S.M. e. a. Hardware Implementation of an Active Learning Self-organizing Neural Network to Predict the Power Fluctuation Events of a Photovoltaic Grid-tied System. Microprocessors and Microsystems. 2022;90:104448
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For citation: Chen Xiaoyu, Du Yang, Almnhalawi Hashim Ali, Velkin V.I., Li Quanpeng. A Review of Photovoltaic System Power Output Forecasting Methods. Bulletin of MPEI. 2025;5:41—50. (in Russian). DOI: 10.24160/1993-6982-2025-5-41-50
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Conflict of interests: the authors declare no conflict of interest
2. Cui H., Li B.I. Research on Photovoltaic Power Forecasting Model Based on Hybrid Neural Network // Power System Protection and Control. 2021. V. 49(13). Pp. 142—149.
3. Wang. H. e. a. Global Sensitivity Analysis for Islanded Microgrid Based on Sparse Polynomial Chaos Expansion // Automation of Electric Power Systems. 2019. V. 43(10). Pp. 44—57.
4. Liu Q. e. a. Deep Learning Photovoltaic Power Generation Model Based on Time Series // Power System Protection and Control. 2021. V. 49(19). Pp. 87—98.
5. Ji X. e. a. Short-term Photovoltaic Power Forecasting Based on MIE-LSTM // Power System Protection and Control. 2020. V. 48(7). Pp. 50—57.
6. Bozorg M. e. a. Bayesian Bootstrap Quantile Regression for Probabilistic Photovoltaic Power Forecasting // Protection and Control of Modern Power Systems. 2020. V. 5. P. 21.
7. Jakoplić A., Franković D., Havelka J., Bulat H. Short-term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning // Energies. 2023. V. 16(14). P. 5428.
8. Stüber M. e. a. Forecast Quality of Physics-based and Data-driven PV Performance Models for a Small-scale PV System // Frontiers Energy Research. 2021. V. 9. P. 639346.
9. Duman Altan A., Diken B., Kayisoglu B. Prediction of Photovoltaic Panel Power Outputs Using Time Series and Artificial Neural Network Methods // J. Tekirdag Agricultural Faculty. 2021. V. 18. Pp. 457—469.
10. Farah S., Boland J. Time Series Model For Real-time Forecasting of Australian Photovoltaic Solar Farms Power Output // J. Renewable and Sustainable Energy. 2021. V. 13(4). P. 046102.
11. Saint-Drenan Y-M. e. a. Bayesian Parameterisation of a Regional Photovoltaic Model — Application to Forecasting // Solar Energy. 2019. V. 188. Pp. 760—774.
12. Jogunuri S. e. a. Random Forest Machine Learning Algorithm Based Seasonal Multi-step Ahead Short-term Solar Photovoltaic Power Output Forecasting // IET Renewable Power Generation. 2024. V. 19(1). Pp. 1—16.
13. Venugopal V., Sun Y., Brandt A.R. Short-term Solar PV Forecasting Using Computer Vision: The Search for Optimal CNN Architectures for Incorporating Sky Images and PV Generation History // J. Renewable and Sustainable Energy. 2019. V. 11(6). P. 066102.
14. Wai R-J., Lai P-X. Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data // Energies. 2022. V. 15(10). P. 3838.
15. Almaghrabi S., Rana M., Hamilton M., Rahaman M.S. Spatially Aggregated Photovoltaic Power Prediction Using Wavelet and Convolutional Neural Networks // Proc. Intern. Joint Conf. Neural Networks (IJCNN). Shenzhen, 2021. Pp. 1—8.
16. Wang Y., Wang X., Chen W., Yan K. Short-term Photovoltaic Output Forecasting Method Using LSTM Neural Network Considering Spatial Correlation // J. Automation Power System. 2020. V. 32. Pp. 78—85.
17. Зацаринная Ю.Н. и др. Прогнозирование выработки электроэнергии от ВИЭ методами машинного обучения // Известия высших учебных заведений. Серия «Проблемы энергетики». 2023. № 25(3). С. 81—92.
18. Wang S. e. a. A New Hybrid Short-term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM // Energies. 2020. V. 13(1). P. 87.
19. Құттыбай Н.Б., Айтбекова Ш.Б., Қошқарбай Н.Ж., Болатбек А.Б., Жоламанов Б.Н. Прогнозирование выходной мощности фотоэлектрической системына основе нейронных сетей // Информационно-коммуникационные и химические технологии. 2024. № 1(22). С. 84—96.
20. Chen Z.B. e. a. Photovoltaic Power Ultra Short-term Prediction Model Based on Ground Cloud Image and Radial Basis Function Artificial Neural Network // Proc. Chinese Soc. Electrical and Electronics Eng. Conf. 2015. V. 35. Pp. 561—567.
21. Ozoegwu C.G. Artificial Neural Network Forecast of Monthly Mean Daily Global Solar Radiation of Selected Locations Based on Time Series and Month Number // J. Cleaner Production. 2019. V. 216. Pp. 1—13.
22. Heo J., Jung J., Kim B., Han S. Digital Elevation Model-based Convolutional Neural Network Modeling for Searching of High Solar Energy Regions // Appl. Energy. 2020. V. 262. P. 114588.
23. Aslam M., Lee J-M., Kim H-S., Lee S-J., Hong S. Deep Learning Models for Long-term Solar Radiation Forecasting Considering Microgrid Installation: a Comparative Study // Energies. 2020. V. 13(1). P. 147.
24. Jung Y., Jung J., Kim B., Han S. Long Short-term Memory Recurrent Neural Network for Modeling Temporal Patterns in Long-term Power Forecasting for Solar PV Facilities: Case Study of South Korea // J. Cleaner Production. 2020. V. 250. P. 119476.
25. 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.
26. 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.
27. Моргоева А.Д., Моргоев И.Д., Клюев Р.В., Кочковская С.С. Прогнозирование почасовой выработки электроэнергии солнечной электростанцией с помощью алгоритмов машинного обучения // Известия Томского политехн. ун-та. Серия «Инжиниринг георесурсов». 2023. № 334(12). С. 7—19.
28. Kumari P., Toshniwal D. Long Short Term Memory-сonvolutional Neural Network Based Deep Hybrid Approach for Solar Irradiance Forecasting // Appl. Energy. 2021. V. 295. P. 117061.
29. Basaran K., Çelikten A., Bulut H. A Short-term Photovoltaic Output Power Forecasting Based on Ensemble Algorithms Using Hyperparameter Optimization // Electrical Eng. 2024. V. 106. Pp. 5319—5337.
30. Hu K. e. a. Short-term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer // Sustainability. 2024. V. 16(14). P. 5940.
31. 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.
32. Eom H., Son Y., Choi S. Feature-selective Ensemble Learning-based Long-term Regional PV Generation Forecasting // IEEE Access. 2020. V. 8. Pp. 54620—54630.
33. Jiao L. e. a. Spatial Clustering Method for Large-scale Distributed User Photovoltaics Based on Spatial Correlation // J. Automation Power System. 2019. V. 43. Pp. 97—102.
34. Goh S.M. e. a. Hardware Implementation of an Active Learning Self-organizing Neural Network to Predict the Power Fluctuation Events of a Photovoltaic Grid-tied System // Microprocessors and Microsystems. 2022. V. 90. P. 104448
---
Для цитирования: Чэнь Сяоюй, Ду Ян, Альмнхалави Хашим Али, Велькин В.И., Ли Цюаньпэн. Обзор методов прогнозирования мощности фотоэлектрических систем // Вестник МЭИ. 2025. № 5. С. 41—50. DOI: 10.24160/1993-6982-2025-5-41-50
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Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Kuo W.-C., Chen C.-H., Hua S.-H., Wang C.-C. Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System. Appl. Sci. 2022;12(15):7529.
2. Cui H., Li B.I. Research on Photovoltaic Power Forecasting Model Based on Hybrid Neural Network. Power System Protection and Control. 2021;49(13):142—149.
3. Wang. H. e. a. Global Sensitivity Analysis for Islanded Microgrid Based on Sparse Polynomial Chaos Expansion. Automation of Electric Power Systems. 2019;43(10):44—57.
4. Liu Q. e. a. Deep Learning Photovoltaic Power Generation Model Based on Time Series. Power System Protection and Control. 2021;49(19):87—98.
5. Ji X. e. a. Short-term Photovoltaic Power Forecasting Based on MIE-LSTM. Power System Protection and Control. 2020;48(7):50—57.
6. Bozorg M. e. a. Bayesian Bootstrap Quantile Regression for Probabilistic Photovoltaic Power Forecasting. Protection and Control of Modern Power Systems. 2020;5:21.
7. Jakoplić A., Franković D., Havelka J., Bulat H. Short-term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning. Energies. 2023;16(14):5428.
8. Stüber M. e. a. Forecast Quality of Physics-based and Data-driven PV Performance Models for a Small-scale PV System. Frontiers Energy Research. 2021;9:639346.
9. Duman Altan A., Diken B., Kayisoglu B. Prediction of Photovoltaic Panel Power Outputs Using Time Series and Artificial Neural Network Methods. J. Tekirdag Agricultural Faculty. 2021;18:457—469.
10. Farah S., Boland J. Time Series Model For Real-time Forecasting of Australian Photovoltaic Solar Farms Power Output. J. Renewable and Sustainable Energy. 2021;13(4):046102.
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For citation: Chen Xiaoyu, Du Yang, Almnhalawi Hashim Ali, Velkin V.I., Li Quanpeng. A Review of Photovoltaic System Power Output Forecasting Methods. Bulletin of MPEI. 2025;5:41—50. (in Russian). DOI: 10.24160/1993-6982-2025-5-41-50
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Conflict of interests: the authors declare no conflict of interest
Опубликован
2025-06-24
Выпуск
Раздел
Энергетические системы и комплексы (технические науки) (2.4.5)
