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Multi-Wind Turbine Wind Speed Prediction Based on Weighted Diffusion Graph Convolution and Gated Attention Network

Author

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  • Yakai Qiao

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Hui Chen

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Bo Fu

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

Abstract

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively.

Suggested Citation

  • Yakai Qiao & Hui Chen & Bo Fu, 2024. "Multi-Wind Turbine Wind Speed Prediction Based on Weighted Diffusion Graph Convolution and Gated Attention Network," Energies, MDPI, vol. 17(7), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1658-:d:1367332
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    References listed on IDEAS

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    1. Exizidis, Lazaros & Kazempour, S. Jalal & Pinson, Pierre & de Greve, Zacharie & Vallée, François, 2016. "Sharing wind power forecasts in electricity markets: A numerical analysis," Applied Energy, Elsevier, vol. 176(C), pages 65-73.
    2. Jing Lu & Hafiz Mutee-ur-Rehman & Saima Nazeer & Xuemei An & Tabasam Rashid, 2022. "The Edge-Weighted Graph Entropy Using Redefined Zagreb Indices," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, March.
    3. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
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