Wind farm combination forecasting model based on dynamic graph attention

Xuechao Liao; Yiqun Cheng

doi:10.1117/12.2682328

20 June 2023 Wind farm combination forecasting model based on dynamic graph attention

Xuechao Liao, Yiqun Cheng

Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 1271518 (2023) https://doi.org/10.1117/12.2682328
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China

Abstract

In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance.

Citation Download Citation

Xuechao Liao and Yiqun Cheng "Wind farm combination forecasting model based on dynamic graph attention", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 1271518 (20 June 2023); https://doi.org/10.1117/12.2682328

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