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- W4376457962 abstract "The large-scale access of wind power generation units brings hidden dangers to the safe and stable operation of power systems, and accurate wind power prediction can somewhat abate the adverse effects of new energy volatility. With the development of wind farm clusters, the power of each wind farm forms a high-dimensional spatial-temporal graph with complex coupling due to natural factors such as location, distance and weather. In recent years, the graph neural network has received increasing attention due to its excellent performance on graph structure data, so this paper studies wind power prediction methods based on graph convolutional networks, and single-step prediction models and multi-step prediction models are established using graph convolutional networks to achieve collaborative prediction of multiple wind farms. Compared with the existing solutions, the proposed method obtains better ultra-short-term wind power prediction performance in terms of accuracy." @default.
- W4376457962 created "2023-05-14" @default.
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- W4376457962 date "2022-11-11" @default.
- W4376457962 modified "2023-10-02" @default.
- W4376457962 title "On the Use of Spatial-temporal Feature Graph Convolutional Network for Ultra-Short Term Prediction of Wind Power Generation" @default.
- W4376457962 doi "https://doi.org/10.1109/ei256261.2022.10117115" @default.
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