Оценка влияния способов агрегации ветроэнергетических установок на эффективность краткосрочного прогнозирования выработки электроэнергии
Ключевые слова:
прогнозирование выработки электроэнергии, агрегация ветроустановок, кластеризация, алгоритм динамической трансформации временной шкалы, ветроэлектрическая станция
Аннотация
Переменный характер энергии ветра требует высокой точности прогнозирования выработки ветроэлектрических станций для оптового рынка электроэнергии. Мощность, выдаваемая отдельными ветроэнергетическими установками (ВЭУ), объединенными в ветроэлектрические станции, может значительно отличаться из-за эффекта «затенения» ветроустановок друг другом, препятствий и др. Для учета факта затенения и повышения точности прогнозов вместо построения одной модели для всей ветроэлектрической станции могут применяться несколько прогнозных моделей для групп ВЭУ и отдельных ВЭУ. В статье представлено исследование способов агрегации отдельных ВЭУ для повышения эффективности краткосрочного прогнозирования выработки ветроэлектрических станций. Предложен новый способ объединения ветроустановок в группы с помощью метода иерархической агломеративной кластеризации по расстоянию, основанному на методе динамической трансформации временной шкалы (DTW-расстоянию), который позволил снизить среднеквадратичную ошибку прогноза на 8,7 % по отношению к прогнозу на уровне ветроэлектрической станции.
Литература
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3. НП Совет рынка. Регламент подачи ценовых заявок участниками оптового рынка. Приложение № 5 к Договору о присоединении к торговой системе оптового рынка: с изм. от 24 июля 2024 г. [Электрон. ресурс], URL: https://www.np-sr.ru/ru/regulation/joining/reglaments/1962 (дата обращения: 21.08.2024).
4. НП Совет рынка. Регламент проведения конкурентного отбора ценовых заявок на сутки вперед Приложение № 7 к Договору о присоединении к торговой системе оптового рынка: с изм. от 22 июля 2024 г. [Электрон. ресурс], URL: https://www.np-sr.ru/sites/default/files/sr_regulation/reglaments/r7_01012026_26112024.docx (дата обращения: 21.08.2024).
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7. Gensler A. Wind Power Ensemble Forecasting: Performance Measures and Ensemble Architectures for Deterministic and Probabilistic Forecasts. Kassel: Kassel University Press, 2019, 214 p.
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9. Gasch R., Twele J. Wind Power Plants: Fundamentals, Design, Construction and Operation. Berlin: Springer Science & Business Media, 2011, 548 p.
10. Renewable Electricity and the Grid. The Challenge of Variability / Ed. G. Boyle. London: Routledge, 2009, 244 p.
11. Ismail I. et al. Wake Effect and Power Production of Wind Turbine Arrays. – Modern Applied Science, 2015, vol. 9, No, DOI: 10.5539/mas.v9n11p77.
12. Dong G. et al. How Far the Wake of a Wind Farm Can Persist for? – Theoretical and Applied Mechanics Letters, 2022, vol. 12, DOI: 10.1016/j.taml.2021.100314.
13. Hanifi S. et al. A Critical Review of Wind Power Forecasting Methods – Past, Present and Future. – Energies, 2020, vol. 13, No. 15, DOI: 10.3390/en13153764.
14. Focken U. et al. Short-Term Prediction of the Aggregated Power Output of Wind Farms – a Statistical Analysis of the Reduction of the Prediction Error by Spatial Smoothing Effects. – Journal of Wind Engineering and Industrial Aerodynamics, 2002, vol. 90, iss. 3, pp. 231–246, DOI: 10.1016/S0167-6105(01)00222-7.
15. Lobo M.G., Sanchez I. Regional Wind Power Forecasting Based on Smoothing Techniques, With Application to the Spanish Peninsular System. – IEEE Transactions on Power Systems, 2012, vol. 27, No. 4, pp. 1990–1997, DOI: 10.1109/TPWRS.2012.2189418.
16. Yakoub G., Mathew S., Leal J. Direct and Indirect Short-Term Aggregated Turbine-And Farm-Level Wind Power Forecasts Integrating Several NWP Sources. – Heliyon, 2023, vol. 9, iss. 11, DOI: 10.1016/j.heliyon.2023.e21479.
17. Liu Y. et al. Clustering Methods of Wind Turbines and Its Application in Short-Term Wind Power Forecasts. – Journal of Renewable and Sustainable Energy, 2014, vol. 6, iss. 5, DOI: 10.1063/1.4898361.
18. Wang Y. et al. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method. – Energies, 2018, vol. 11, No. 4, DOI 10.3390/en11040854.
19. Lloyd S. Least Squares Quantization in PCM. – IEEE Transactions on Information Theory, 1982, vol. 28, No. 2, pp. 129–137, DOI: 10.1109/TIT.1982.1056489.
20. Arthur D., Vassilvitskii S. K-Means++: The Advantages of Careful Seeding. – 8th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA, 2007, DOI: 10.1145/1283383.1283494.
21. Ward Jr J.H. Hierarchical Grouping to Optimize an Objective Function. – Journal of the American Statistical Association, 1963, vol. 58, No. 301, pp. 236–244, DOI: 10.1080/01621459.1963.10500845.
22. Miyamoto S. et al. Ward Method of Hierarchical Clustering for Non-Euclidean Similarity Measures. – 7th International Conference of Soft Computing and Pattern Recognition, 2015, pp. 60–63, DOI: 10.1109/SOCPAR.2015.7492784.
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24. Nakagawa K., Imamura M., Yoshida K. Stock Price Prediction Using K‐Medoids Clustering with Indexing Dynamic Time Warping. – Electronics and Communications in Japan, 2019, vol. 138, No. 8, pp. 986–991, DOI: 10.1541/ieejeiss.138.986.
25. Simmhan Y., Noor M.U. Scalable Prediction of Energy Consumption Using Incremental Time Series Clustering. – IEEE International Conference on Big Data, 2013, pp. 29–36, DOI: 10.1109/BigData.2013.6691774.
26. Sakoe H., Chiba S. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. – IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978, vol. 26, No. 1, pp. 43–49, DOI: 10.1109/TASSP.1978.1163055.
27. Zhang Z. et al. Dynamic Time Warping Under Limited Warping Path Length. – Information Sciences, 2017, vol. 393, pp. 91–107. DOI: 10.1016/j.ins.2017.02.018.
28. MathWorks. Signal Processing Toolbox [Электрон. ресурс], URL: https://www.mathworks.com/help/stats/index.html (дата обращения: 25.11.2024).
29. Friedman J.H. Greedy Function Approximation: A Gradient Boosting Machine. – The Annals of Statistics, 2001, vol. 29, No. 5, pp. 1189–1232. DOI: 10.1214/aos/1013203451.
30. Feurer M., Hutter F. Hyperparameter Optimization. – Automated Machine Learning. Springer, 2019, pp. 3–33, DOI: 10.1007/ 978-3-030-05318-5_1.
31. Piotrowski P. et al. Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors. – Energies, 2022, vol. 15, No. 24, DOI: 10.3390/en15249657.
32. Snegirev D.A. et al. The Selection of Machine Learning Model and Its Hyperparameters Using Bayesian Optimization for Short-Term Wind Power Forecasting. – Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC), 2023, pp. 18–23, DOI: 10.1109/BUSSEC59406.2023.10296274.
33. Snegirev D.A. et al. Short-Term Wind Power Forecasting Based on Gaussian Process Regression. – 6th International Scientific and Technical Conference on Relay Protection and Automation (RPA), 2023, DOI: 10.1109/RPA59835.2023.10319865.
#
1. Ilyushin P.V. Vestnik Moskovskogo energeticheskogo instituta – in Russ. (Bulletin of the Moscow Power Engineering Institute), 2022, No. 4, pp. 98–107.
2. International Energy Agency. System Integration of Rene-wables: An Update on Best Practice. [Electron. resource], URL: https://www.iea.org/reports/system-integration-of-renewables (Access on 07.12.2022).
3. NP Market Council [Electron. resource], URL: https://www.np-sr.ru/ru/regulation/joining/reglaments/1962 (Access on 21.08.2024).
4. NP Market Council [Electron. resource], URL: https://www.np-sr.ru/sites/default/files/sr_regulation/reglaments/r7_01012026_ 26112024.docx (Access on 21.08.2024).
5. Obuhov S.G. Sistemy generirovaniya elektricheskoy energii s ispol’zovaniem vozobnovlyaemyh energoresursov (Electricity Generation Systems Using Renewable Energy Resources). Tomsk: Izd-vo Tomskogo politehnicheskogo universiteta, 2008, 140 p.
6. Schaffarczyk A. et al. Understanding Wind Power Technology: Theory, Deployment and Optimization. Hoboken: John Wiley & Sons, 2014, 488 p.
7. Gensler A. Wind Power Ensemble Forecasting: Performance Measures and Ensemble Architectures for Deterministic and Probabilistic Forecasts. Kassel: Kassel University Press, 2019, 214 p.
8. Bianchi F.D., Mantz R.J., Battista H. Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design. London: Springer, 2007, 228 p.
9. Gasch R., Twele J. Wind Power Plants: Fundamentals, Design, Construction and Operation. Berlin: Springer Science & Business Media, 2011, 548 p.
10. Renewable Electricity and the Grid. The Challenge of Variability / Ed. G. Boyle. London: Routledge, 2009, 244 p.
11. Ismail I. et al. Wake Effect and Power Production of Wind Turbine Arrays. – Modern Applied Science, 2015, vol. 9, No, DOI: 10.5539/mas.v9n11p77.
12. Dong G. et al. How Far the Wake of a Wind Farm Can Persist for? – Theoretical and Applied Mechanics Letters, 2022, vol. 12, DOI: 10.1016/j.taml.2021.100314.
13. Hanifi S. et al. A Critical Review of Wind Power Forecasting Methods – Past, Present and Future. – Energies, 2020, vol. 13, No. 15, DOI: 10.3390/en13153764.
14. Focken U. et al. Short-Term Prediction of the Aggregated Power Output of Wind Farms – a Statistical Analysis of the Reduction of the Prediction Error by Spatial Smoothing Effects. – Journal of Wind Engineering and Industrial Aerodynamics, 2002, vol. 90, iss. 3, pp. 231–246, DOI: 10.1016/S0167-6105(01)00222-7.
15. Lobo M.G., Sanchez I. Regional Wind Power Forecasting Based on Smoothing Techniques, With Application to the Spanish Peninsular System. – IEEE Transactions on Power Systems, 2012, vol. 27, No. 4, pp. 1990–1997, DOI: 10.1109/TPWRS.2012.2189418.
16. Yakoub G., Mathew S., Leal J. Direct and Indirect Short-Term Aggregated Turbine-And Farm-Level Wind Power Forecasts Integrating Several NWP Sources. – Heliyon, 2023, vol. 9, iss. 11, DOI: 10.1016/j.heliyon.2023.e21479.
17. Liu Y. et al. Clustering Methods of Wind Turbines and Its Application in Short-Term Wind Power Forecasts. – Journal of Renewable and Sustainable Energy, 2014, vol. 6, iss. 5, DOI: 10.1063/ 1.4898361.
18. Wang Y. et al. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method. – Energies, 2018, vol. 11, No. 4, DOI 10.3390/en11040854.
19. Lloyd S. Least Squares Quantization in PCM. – IEEE Transactions on Information Theory, 1982, vol. 28, No. 2, pp. 129–137, DOI: 10.1109/TIT.1982.1056489.
20. Arthur D., Vassilvitskii S. K-Means++: The Advantages of Careful Seeding. – 8th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA, 2007, DOI: 10.1145/1283383.1283494.
21. Ward Jr J.H. Hierarchical Grouping to Optimize an Objective Function. – Journal of the American Statistical Association, 1963, vol. 58, No. 301, pp. 236–244, DOI: 10.1080/01621459.1963.10500845.
22. Miyamoto S. et al. Ward Method of Hierarchical Clustering for Non-Euclidean Similarity Measures. – 7th International Conference of Soft Computing and Pattern Recognition, 2015, pp. 60–63, DOI: 10.1109/SOCPAR.2015.7492784.
23. MathWorks. Statistics and Machine Learning Toolbox: Analyze and Model Data Using Statistics and Machine Learning [Electron. resource], URL: https://www.mathworks.com/help/stats/index.html (Access on 25.11.2024).
24. Nakagawa K., Imamura M., Yoshida K. Stock Price Prediction Using K‐Medoids Clustering with Indexing Dynamic Time Warping. – Electronics and Communications in Japan, 2019, vol. 138, No. 8, pp. 986–991, DOI: 10.1541/ieejeiss.138.986.
25. Simmhan Y., Noor M.U. Scalable Prediction of Energy Consumption Using Incremental Time Series Clustering. – IEEE International Conference on Big Data, 2013, pp. 29–36, DOI: 10.1109/BigData.2013.6691774.
26. Sakoe H., Chiba S. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. – IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978, vol. 26, No. 1, pp. 43–49, DOI: 10.1109/TASSP.1978.1163055.
27. Zhang Z. et al. Dynamic Time Warping Under Limited War-ping Path Length. – Information Sciences, 2017, vol. 393, pp. 91–107. DOI: 10.1016/j.ins.2017.02.018.
28. MathWorks. Signal Processing Toolbox [Electron. resource], URL: https://www.mathworks.com/help/stats/index.html (Access on 25.11.2024).
29. Friedman J.H. Greedy Function Approximation: A Gradient Boosting Machine. – The Annals of Statistics, 2001, vol. 29, No. 5, pp. 1189–1232. DOI: 10.1214/aos/1013203451.
30. Feurer M., Hutter F. Hyperparameter Optimization. – Automated Machine Learning. Springer, 2019, pp. 3–33, DOI: 10.1007/ 978-3-030-05318-5_1.
31. Piotrowski P. et al. Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors. – Energies, 2022, vol. 15, No. 24, DOI: 10.3390/en15249657.
32. Snegirev D.A. et al. The Selection of Machine Learning Model and Its Hyperparameters Using Bayesian Optimization for Short-Term Wind Power Forecasting. – Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC), 2023, pp. 18–23, DOI: 10.1109/BUSSEC59406.2023.10296274.
33. Snegirev D.A. et al. Short-Term Wind Power Forecasting Based on Gaussian Process Regression. – 6th International Scientific and Technical Conference on Relay Protection and Automation (RPA), 2023, DOI: 10.1109/RPA59835.2023.10319865
2. International Energy Agency. System Integration of Renewables: An Update on Best Practice. [Электрон. ресурс], URL: https://www.iea.org/reports/system-integration-of-renewables (дата обращения: 07.12.2022).
3. НП Совет рынка. Регламент подачи ценовых заявок участниками оптового рынка. Приложение № 5 к Договору о присоединении к торговой системе оптового рынка: с изм. от 24 июля 2024 г. [Электрон. ресурс], URL: https://www.np-sr.ru/ru/regulation/joining/reglaments/1962 (дата обращения: 21.08.2024).
4. НП Совет рынка. Регламент проведения конкурентного отбора ценовых заявок на сутки вперед Приложение № 7 к Договору о присоединении к торговой системе оптового рынка: с изм. от 22 июля 2024 г. [Электрон. ресурс], URL: https://www.np-sr.ru/sites/default/files/sr_regulation/reglaments/r7_01012026_26112024.docx (дата обращения: 21.08.2024).
5. Обухов С.Г. Системы генерирования электрической энергии с использованием возобновляемых энергоресурсов. Томск: Изд-во Томского политехнического университета, 2008, 140 с.
6. Schaffarczyk A. et al. Understanding Wind Power Technology: Theory, Deployment and Optimization. Hoboken: John Wiley & Sons, 2014, 488 p.
7. Gensler A. Wind Power Ensemble Forecasting: Performance Measures and Ensemble Architectures for Deterministic and Probabilistic Forecasts. Kassel: Kassel University Press, 2019, 214 p.
8. Bianchi F.D., Mantz R.J., Battista H. Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design. London: Springer, 2007, 228 p.
9. Gasch R., Twele J. Wind Power Plants: Fundamentals, Design, Construction and Operation. Berlin: Springer Science & Business Media, 2011, 548 p.
10. Renewable Electricity and the Grid. The Challenge of Variability / Ed. G. Boyle. London: Routledge, 2009, 244 p.
11. Ismail I. et al. Wake Effect and Power Production of Wind Turbine Arrays. – Modern Applied Science, 2015, vol. 9, No, DOI: 10.5539/mas.v9n11p77.
12. Dong G. et al. How Far the Wake of a Wind Farm Can Persist for? – Theoretical and Applied Mechanics Letters, 2022, vol. 12, DOI: 10.1016/j.taml.2021.100314.
13. Hanifi S. et al. A Critical Review of Wind Power Forecasting Methods – Past, Present and Future. – Energies, 2020, vol. 13, No. 15, DOI: 10.3390/en13153764.
14. Focken U. et al. Short-Term Prediction of the Aggregated Power Output of Wind Farms – a Statistical Analysis of the Reduction of the Prediction Error by Spatial Smoothing Effects. – Journal of Wind Engineering and Industrial Aerodynamics, 2002, vol. 90, iss. 3, pp. 231–246, DOI: 10.1016/S0167-6105(01)00222-7.
15. Lobo M.G., Sanchez I. Regional Wind Power Forecasting Based on Smoothing Techniques, With Application to the Spanish Peninsular System. – IEEE Transactions on Power Systems, 2012, vol. 27, No. 4, pp. 1990–1997, DOI: 10.1109/TPWRS.2012.2189418.
16. Yakoub G., Mathew S., Leal J. Direct and Indirect Short-Term Aggregated Turbine-And Farm-Level Wind Power Forecasts Integrating Several NWP Sources. – Heliyon, 2023, vol. 9, iss. 11, DOI: 10.1016/j.heliyon.2023.e21479.
17. Liu Y. et al. Clustering Methods of Wind Turbines and Its Application in Short-Term Wind Power Forecasts. – Journal of Renewable and Sustainable Energy, 2014, vol. 6, iss. 5, DOI: 10.1063/1.4898361.
18. Wang Y. et al. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method. – Energies, 2018, vol. 11, No. 4, DOI 10.3390/en11040854.
19. Lloyd S. Least Squares Quantization in PCM. – IEEE Transactions on Information Theory, 1982, vol. 28, No. 2, pp. 129–137, DOI: 10.1109/TIT.1982.1056489.
20. Arthur D., Vassilvitskii S. K-Means++: The Advantages of Careful Seeding. – 8th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA, 2007, DOI: 10.1145/1283383.1283494.
21. Ward Jr J.H. Hierarchical Grouping to Optimize an Objective Function. – Journal of the American Statistical Association, 1963, vol. 58, No. 301, pp. 236–244, DOI: 10.1080/01621459.1963.10500845.
22. Miyamoto S. et al. Ward Method of Hierarchical Clustering for Non-Euclidean Similarity Measures. – 7th International Conference of Soft Computing and Pattern Recognition, 2015, pp. 60–63, DOI: 10.1109/SOCPAR.2015.7492784.
23. MathWorks. Statistics and Machine Learning Toolbox: Analyze and Model Data Using Statistics and Machine Learning [Электрон. ресурс], URL: https://www.mathworks.com/help/stats/index.html (дата обращения: 25.11.2024).
24. Nakagawa K., Imamura M., Yoshida K. Stock Price Prediction Using K‐Medoids Clustering with Indexing Dynamic Time Warping. – Electronics and Communications in Japan, 2019, vol. 138, No. 8, pp. 986–991, DOI: 10.1541/ieejeiss.138.986.
25. Simmhan Y., Noor M.U. Scalable Prediction of Energy Consumption Using Incremental Time Series Clustering. – IEEE International Conference on Big Data, 2013, pp. 29–36, DOI: 10.1109/BigData.2013.6691774.
26. Sakoe H., Chiba S. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. – IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978, vol. 26, No. 1, pp. 43–49, DOI: 10.1109/TASSP.1978.1163055.
27. Zhang Z. et al. Dynamic Time Warping Under Limited Warping Path Length. – Information Sciences, 2017, vol. 393, pp. 91–107. DOI: 10.1016/j.ins.2017.02.018.
28. MathWorks. Signal Processing Toolbox [Электрон. ресурс], URL: https://www.mathworks.com/help/stats/index.html (дата обращения: 25.11.2024).
29. Friedman J.H. Greedy Function Approximation: A Gradient Boosting Machine. – The Annals of Statistics, 2001, vol. 29, No. 5, pp. 1189–1232. DOI: 10.1214/aos/1013203451.
30. Feurer M., Hutter F. Hyperparameter Optimization. – Automated Machine Learning. Springer, 2019, pp. 3–33, DOI: 10.1007/ 978-3-030-05318-5_1.
31. Piotrowski P. et al. Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors. – Energies, 2022, vol. 15, No. 24, DOI: 10.3390/en15249657.
32. Snegirev D.A. et al. The Selection of Machine Learning Model and Its Hyperparameters Using Bayesian Optimization for Short-Term Wind Power Forecasting. – Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC), 2023, pp. 18–23, DOI: 10.1109/BUSSEC59406.2023.10296274.
33. Snegirev D.A. et al. Short-Term Wind Power Forecasting Based on Gaussian Process Regression. – 6th International Scientific and Technical Conference on Relay Protection and Automation (RPA), 2023, DOI: 10.1109/RPA59835.2023.10319865.
#
1. Ilyushin P.V. Vestnik Moskovskogo energeticheskogo instituta – in Russ. (Bulletin of the Moscow Power Engineering Institute), 2022, No. 4, pp. 98–107.
2. International Energy Agency. System Integration of Rene-wables: An Update on Best Practice. [Electron. resource], URL: https://www.iea.org/reports/system-integration-of-renewables (Access on 07.12.2022).
3. NP Market Council [Electron. resource], URL: https://www.np-sr.ru/ru/regulation/joining/reglaments/1962 (Access on 21.08.2024).
4. NP Market Council [Electron. resource], URL: https://www.np-sr.ru/sites/default/files/sr_regulation/reglaments/r7_01012026_ 26112024.docx (Access on 21.08.2024).
5. Obuhov S.G. Sistemy generirovaniya elektricheskoy energii s ispol’zovaniem vozobnovlyaemyh energoresursov (Electricity Generation Systems Using Renewable Energy Resources). Tomsk: Izd-vo Tomskogo politehnicheskogo universiteta, 2008, 140 p.
6. Schaffarczyk A. et al. Understanding Wind Power Technology: Theory, Deployment and Optimization. Hoboken: John Wiley & Sons, 2014, 488 p.
7. Gensler A. Wind Power Ensemble Forecasting: Performance Measures and Ensemble Architectures for Deterministic and Probabilistic Forecasts. Kassel: Kassel University Press, 2019, 214 p.
8. Bianchi F.D., Mantz R.J., Battista H. Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design. London: Springer, 2007, 228 p.
9. Gasch R., Twele J. Wind Power Plants: Fundamentals, Design, Construction and Operation. Berlin: Springer Science & Business Media, 2011, 548 p.
10. Renewable Electricity and the Grid. The Challenge of Variability / Ed. G. Boyle. London: Routledge, 2009, 244 p.
11. Ismail I. et al. Wake Effect and Power Production of Wind Turbine Arrays. – Modern Applied Science, 2015, vol. 9, No, DOI: 10.5539/mas.v9n11p77.
12. Dong G. et al. How Far the Wake of a Wind Farm Can Persist for? – Theoretical and Applied Mechanics Letters, 2022, vol. 12, DOI: 10.1016/j.taml.2021.100314.
13. Hanifi S. et al. A Critical Review of Wind Power Forecasting Methods – Past, Present and Future. – Energies, 2020, vol. 13, No. 15, DOI: 10.3390/en13153764.
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