Fan Xiao1, Xiong Ping1, Yeyang Li2,*, Yusen Xu2, Yiqun Kang1, Dan Liu1, Nianming Zhang1
Energy Engineering, Vol.121, No.2, pp. 359-376, 2024, DOI:10.32604/ee.2023.040887
- 25 January 2024
Abstract The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale. Therefore, wind power forecasting plays a key role in improving the safety and economic benefits of the power grid. This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data. Based on the graph attention network and attention mechanism, the method extracts spatial-temporal characteristics from the data of multiple wind farms. Then, combined with a deep… More >
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