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A hybrid approach to multi-step, short-term wind speed forecasting using correlated features

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  • Sun, Fei
  • Jin, Tongdan

Abstract

Wind power is becoming a main alternative energy source to meet the growing electricity needs. Forecasting wind speed is important to mitigate generation uncertainty and optimize asset utilization. This paper proposes a hybrid wind speed prediction model with multivariate input and multi-step output capability. The model synthesizes linear time series regression with nonlinear machine learning algorithm. The input neurons of the hybrid model are determined by the number of lag observations in autoregressive integrated moving average (ARIMA), and also by correlated meteorological features, such as wind direction, air pressure, humidity, dew point, and temperature. The output neurons are further derived based on the forecasting horizon. The hybrid model is trained, validated, and tested by using 1.73 million hourly meteorological records from three cities with diverse wind profiles. The performance of the model is compared with several existing methods based on root mean square error and mean absolute error. Though the hybrid model does not show obvious advantage in 1-h ahead prediction, it outperforms persistence model, ARIMA, and univariate neural network models in 3-to-24 h ahead prediction. The hybrid model is able to reduce the prediction error by 20% in comparison with univariate neural networks.

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  • Sun, Fei & Jin, Tongdan, 2022. "A hybrid approach to multi-step, short-term wind speed forecasting using correlated features," Renewable Energy, Elsevier, vol. 186(C), pages 742-754.
    • Handle: RePEc:eee:renene:v:186:y:2022:i:c:p:742-754
    DOI: 10.1016/j.renene.2022.01.041
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    2. Fu, Wenlong & Fu, Yuchen & Li, Bailing & Zhang, Hairong & Zhang, Xuanrui & Liu, Jiarui, 2023. "A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 348(C).
    3. Vladimir Simankov & Pavel Buchatskiy & Semen Teploukhov & Stefan Onishchenko & Anatoliy Kazak & Petr Chetyrbok, 2023. "Review of Estimating and Predicting Models of the Wind Energy Amount," Energies, MDPI, vol. 16(16), pages 1-24, August.
    4. Yang, Yang & Lang, Jin & Wu, Jian & Zhang, Yanyan & Su, Lijie & Song, Xiangman, 2022. "Wind speed forecasting with correlation network pruning and augmentation: A two-phase deep learning method," Renewable Energy, Elsevier, vol. 198(C), pages 267-282.
    5. Cui, Xiwen & Yu, Xiaoyu & Niu, Dongxiao, 2024. "The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm a," Energy, Elsevier, vol. 288(C).
    6. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.

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