Методы машинного обучения для задач прогнозирования и максимизации выработки электроэнергии солнечной электростанции
Ключевые слова:
методы машинного обучения, солнечная электростанция, фотоэлектрический модуль, прогнозирование выработки солнечной электростанции, MPPT, сверточные нейросети, глубокие нейросети
Аннотация
Системы прогнозирования и максимизации выработки электроэнергии солнечной электростанции на основе методов машинного обучения повышают эффективность солнечной электростанции и, таким образом, актуальны в соответствии с приоритетным направлением развития науки, технологий и техники в РФ, с приоритетом государственной энергетической политики «Энергетической стратегии на период до 2035 года» и утвержденными правительственной комиссией по высоким технологиям и инновациям технологическими платформами: «Интеллектуальная электроэнергетическая система России», «Малая распределённая энергетика». Системы выработки электроэнергии солнечной электростанции имеют сложную нелинейную динамику с неопределенностями, обусловленными изменением параметров системы и флуктуациями инсоляции. Таким образом, аппроксимировать эту сложную динамику классическими алгоритмами с заданной точностью нельзя, в то время как алгоритмы машинного обучения обеспечивают требуемую точность. Методы машинного обучения становятся ключевыми элементами современных систем прогнозирования и максимизации выработки электроэнергии солнечной электростанции в связи с растущим спросом на высокопроизводительный анализ данных с целью повышения эффективности и надежности солнечной электростанции. При решении задач прогнозирования и максимизации выработки электроэнергии солнечной электростанции алгоритмы машинного обучения в сравнении с традиционными алгоритмами обеспечивают следующие преимущества: требуемую точность решения указанных задач; безопасное и эффективное управления электрическими сетями, интегрирующими солнечные электростанции. В отличие от других обзорных статей, наше исследование: кратко обобщает наши интеллектуальные самоадаптирующиеся интеллектуальные системы прогнозирования и максимизации выработки электроэнергии солнечной электростанции; обобщает аналитический обзор в таблицах, отражающих сравнительный анализ качества, в том числе точности, систем прогнозирования и максимизации выработки электроэнергии солнечной электростанции на основе алгоритмов машинного обучения; оценивает перспективы будущей цифровой трансформации солнечной энергетики в умную солнечную энергетику на основе интегрированных передовых технологий, в том числе машинного обучения.
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Литература
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9. Engel E. A. and Engel N. E. (2019) Tracking system for the maximum power point of a photovoltaic array based on a modified fuzzy neural network. Bulletin of cybernetics. No 3. P. 14–25.
10. Alkhayat G. and Mehmood R. (2021) A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy AI. No 4. P. 100060
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12. Khan P. W. Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources. Energies. No 13. P. 4870.
13. Grimaccia F. [et al.] (2017) ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant. Appl. Sci. No 7. P. 622.
14. Omar M. [et al.] (2016) Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles. In Proceedings of the 2016 IEEE International Conference on Renewable Energy Research and Applications. P. 1152–1157.
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20. Wu Y. K. [et al.] (2022) Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints. Energies. No 15. P. 3320.
21. Tovar M. [et al.] (2020) PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México. Energies. No 13. P. 6512.
22. Zjavka L. and Snasel V. (2022) PV Energy Prediction in 24 h Horizon Using Modular Models Based on Polynomial Conversion of the L-Transform PDE Derivatives in Node-by-NodeEvolved Binary-Tree Networks. Eng. Proc. No 18. P. 34.
23. Pombo D. V. (2022) Bench-marking physics-informed machine learning-based short-term PV-power forecasting tools. Energy Reports. No 8. P. 6512–6520.
24. Pombo D. V. [et al.] (2022) Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning. Sensors. No 22. P. 749.
25. Xiaoying R. [et al.] (2022) Quad-kernel deep convolutional neural network for intra-hour pho-tovoltaic power forecasting. Applied Energy. No 323. P. 119682.
26. Ahn H. K. and Park N. (2021) Deep RNNBased Photovoltaic Power Short-Term Forecast Using Power IoT Sensors. Energies. No 14. P. 436.
27. Wang P. Y. [et al.] (2018) Adaptive Solar Power Forecasting based on Machine Learning Methods. Appl. Sci. No 8. P. 2224.
28. Hossain M. S. and Mahmood H. (2020) Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast. IEEE Access. No 8. P. 172524– 172533.
29. Mittal S. A. (2022) Review on forecasting the photovoltaic power Using Machine Learning. Journal of Physics: Conference Series. No 2286. P. 012010.
30. Xing L. [et al.] (2022) Combining transfer learning and constrained long short-term memory for power generation forecasting of newly-constructed photovoltaic plants. Renewable Energy. No 185. P. 1062–1077.
31. Abdellatif A. [et al.] (2022) Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model. Sustainability. No 14. P. 11083.
32. Zhang H. [et al.] (2022) Stacking Model for Photovoltaic-Power-Generation Prediction. Sustainability. No 14. P. 669.
33. Akhter M. N. [et al.] (2022) An HourAhead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants. Energies. No 15. P. 33.
34. Xing L., Dongxiao Z. and Xu Z. (2022) Combining transfer learning and constrained long short-term memory for power generation forecasting of newly-constructed photovoltaic plants. Renewable Energy. No 185. P. 1062–1077.
35. Zjavka L. (2020) PV power intra-day predictions using PDE models of polynomial networks based on operational calculus. IET Renew. Power Gener. No 14. P. 1405–1412.
36. Zhen Z. [et al.] (2020) Deep learning-based surface irradiance mapping model for solar PV power forecasting using sky image. IEEE Trans. Ind. Appl. No 56. P. 3385–3396.
37. Abdel-Nasser M. and Mahmoud K. (2019) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput. Appl. No 31. P. 2727–2740.
38. Zhang J. [et al.] (2018) Deep photovoltaic nowcasting. Sol. Energy. No 176. P. 267–276.
39. Hossain M. [et al.] (2017) Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. J. Clean. Prod. No 167. P. 395–405.
40. Nespoli A. [et al.] (2019) Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies. No 12. P. 1621.
41. Khan P. W. [et al.] (2020) Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources. Energies. No 13. P. 4870.
42. Khan A. [et al.] (2020) A Comparative Study on Solar Power Forecasting using Ensemble Learning. 4th International E3S Web of Conferences. – 2021. – № 309. – P. 01163.
43. Engel E. А. [et al.] (2020) Photovoltaic Applications on the Basis of Modified Fuzzy Neural Net. Solar Irradiance. Types and Applications, In: NOVA SCIENCE PUBLISHERS, ed Daryl M. Welsh. P. 7–87.
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45. Mod. J. [et al.] (2018) Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks. J. Mod. Power Syst. Clean Energy. No 6. P. 255–267.
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47. Pan C. [et al.] (2018) Very Short-Term Solar Generation Forecasting Based on LSTM with Temporal Attention Mechanism. IEEE 5th International Conference on Computer and Communications (ICCC). P. 267–271.
48. Pelland S. [et al.] (2013) Photovoltaic and Solar Forecasting: State of the Art. ArXiv. No 15. P. 1.
49. ESOLMET-IER instituto de energías renovables. Text: electronic. URL
50. Mayer M. J. and Gróf G. (2021) Extensive comparison of physical models for photovoltaic power forecasting. Appl. Energy. No 283. P. 116239.
51. Elieser M. [et al.] (2021) Prediction of site-specific solar diffuse horizontal irradiance from two input variables in Colombia. Heliyon. No 7. P. 08602.
52. Manajit S. [et al.] (2018) The national solar radiation data base (NSRDB). Renewable and Sustainable Energy Reviews. No 89. P. 51–60.
53. Narvaez G. [et al.] (2021) Machine Learning for Site-Adaptation and Solar Radiation Forecasting. Energies. No 167. P. 333–342
54. Belmahdi B. [et al.] (2022) Comparative optimization of global solar radiation forecasting using machine learning and time series models. Environmental Science and Pollution Research. No 29. P. 14871–14888
55. Faisal A. N. [et al.] (2022) Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh. Results in Engineering. No 13. P. 100365.
56. Guermoui M. [et al.] (2022) A novel ensemble learning approach for hourly global solar radiation forecasting. Neural Computing and Applications. No 34. P. 2983–3005
57. Acikgoz H. (2022) A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Applied Energy. No 305. P. 117912
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Опубликован
2023-09-29
Как цитировать
Энгель, Е. А., & Энгель, Н. Е. (2023). Методы машинного обучения для задач прогнозирования и максимизации выработки электроэнергии солнечной электростанции. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (2), 146-170. https://doi.org/10.17308/sait/1995-5499/2023/2/146-170
Выпуск
Раздел
Интеллектуальные системы, анализ данных и машинное обучение
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