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A multi-location short-term wind speed prediction model based on spatiotemporal joint learning

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  • Xu, Yuanyuan
  • Yang, Genke
  • Luo, Jiliang
  • He, Jianan
  • Sun, Haixin

Abstract

Using temporal and spatial correlations to predict wind speed is still one of the most challenging and least-researched fields in wind speed prediction. How to make full use of the spatial correlation of wind speed between adjacent wind turbines to improve the accuracy of wind speed prediction is significant. Therefore, to address the challenges of making multi-location, short-term wind speed predictions, we propose a multi-location wind speed prediction model based on spatiotemporal joint learning, which called Mixed-SqueezeNet-BiGRU. By designing the SqueezeNet model with a deformable convolutional network, the spatial correlation of a multi-location time series is learned by using the spatial wind speed matrix; by introducing the Channel Shuffle operation of ShuffleNet, the characteristics of multi-location and multi-channel are output to ensure the orderly flow of information among the channels. To describe the dynamic information of the time series, a time series dynamic network model based on the bi-directional gating cycle unit, BiGRU, is introduced to model the long-term and nonlinear dependence of the time series. Experiments were carried out on three wind farm's wind speed dataset to verify that the Mixed-SqueezeNet-BiGRU short-term wind speed prediction model has a better prediction performance than some advanced methods.

Suggested Citation

  • Xu, Yuanyuan & Yang, Genke & Luo, Jiliang & He, Jianan & Sun, Haixin, 2022. "A multi-location short-term wind speed prediction model based on spatiotemporal joint learning," Renewable Energy, Elsevier, vol. 183(C), pages 148-159.
  • Handle: RePEc:eee:renene:v:183:y:2022:i:c:p:148-159
    DOI: 10.1016/j.renene.2021.10.075
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    References listed on IDEAS

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    1. Yuanyuan Xu & Tianhe Yao & Genke Yang, 2019. "An EMD-SVM model with error compensation for short-term wind speed forecasting," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 18(2/3), pages 171-181.
    2. Yuanyuan Xu & Genke Yang, 2020. "A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning," Complexity, Hindawi, vol. 2020, pages 1-13, November.
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    5. Yuanyuan Xu & Genke Yang & Jiliang Luo & Jianan He, 2020. "An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, October.
    6. Qiaomu Zhu & Jinfu Chen & Lin Zhu & Xianzhong Duan & Yilu Liu, 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach," Energies, MDPI, vol. 11(4), pages 1-18, March.
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    Cited by:

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    3. Chen, Wenhe & Zhou, Hanting & Cheng, Longsheng & Xia, Min, 2023. "Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention," Energy, Elsevier, vol. 278(PB).
    4. Lars Ødegaard Bentsen & Narada Dilp Warakagoda & Roy Stenbro & Paal Engelstad, 2023. "A Unified Graph Formulation for Spatio-Temporal Wind Forecasting," Energies, MDPI, vol. 16(20), pages 1-23, October.
    5. Li, Yang & Shen, Xiaojun & Zhou, Chongcheng, 2023. "Dynamic multi-turbines spatiotemporal correlation model enabled digital twin technology for real-time wind speed prediction," Renewable Energy, Elsevier, vol. 203(C), pages 841-853.
    6. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    7. Lagomarsino-Oneto, Daniele & Meanti, Giacomo & Pagliana, Nicolò & Verri, Alessandro & Mazzino, Andrea & Rosasco, Lorenzo & Seminara, Agnese, 2023. "Physics informed machine learning for wind speed prediction," Energy, Elsevier, vol. 268(C).

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