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Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance

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  • Ogliari, Emanuele
  • Guilizzoni, Manfredo
  • Giglio, Alessandro
  • Pretto, Silvia

Abstract

In the last decade, wind has experienced a strong expansion reaching 591 GW (2018) of installed capacity worldwide. The higher penetration of variable renewable energy sources (wind and solar) has led to a growing demand for reliable forecast methods, to properly integrate these sources in the electric grid, decreasing the cost of electricity production and power curtailments. The present work proposes diverse wind power predictive approaches based on a physical model, artificial neural networks and an hybridization of the two. The time series used is composed of two-years hourly measurements of a wind farm in Italy, consisting of 24 wind turbines with a nominal power of 0.66 MW. To ensure an adequate reliability and robustness of the results obtained from the performance evaluation, it was chosen to use eight different error metrics and to evaluate the accuracy considering two different predictive situations (yearly and daily), using the persistence model as benchmark. The evaluations of predictive performances, regarding both the analyses, confirmed the superiority of data-driven approaches in the daily wind power prediction. More in detail, the hybrid model managed to reduce the MAE, the NRMSE and the SS values, compared to persistence, by 50%, 47.82% and 47.68%, respectively.

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  • Ogliari, Emanuele & Guilizzoni, Manfredo & Giglio, Alessandro & Pretto, Silvia, 2021. "Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance," Renewable Energy, Elsevier, vol. 178(C), pages 1466-1474.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1466-1474
    DOI: 10.1016/j.renene.2021.06.108
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    3. Han, Yixiao & Liao, Yanfen & Ma, Xiaoqian & Guo, Xing & Li, Changxin & Liu, Xinyu, 2023. "Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network," Renewable Energy, Elsevier, vol. 215(C).
    4. Oliveira, Augusto Cesar Laviola de & Renato, Natalia dos Santos & Martins, Marcio Arêdes & Mendonça, Isabela Miranda de & Moraes, Camile Arêdes & Lago, Lucas Fernandes Rocha, 2023. "Renewable energy solutions based on artificial intelligence for farms in the state of Minas Gerais, Brazil: Analysis and proposition," Renewable Energy, Elsevier, vol. 204(C), pages 24-38.
    5. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
    6. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.

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