Multi-Step Ultra-Short-Term Wind Speed Prediction Based on Decomposition and Reconstruction of Time-Spatial Correlation
Pan Chao1, Li Runyu1, Cai Guowei1, Wang Dian1, Zhang Yonghui2
1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China; 2. Songhuajiang Hydropower Co. Ltd Jilin Baishan Power Plant Jilin 132400 China
Abstract：Considering the spatial correlation of wind speed makes multi-step prediction,which is a research hotspot of large-scale wind power grid integration. This paper adopts an improved multi-position multi-step wind speed prediction method. First, a decomposition and reconstruction strategy of wind speed matrix time-spatial correlation is proposed, and the gray correlation analysis is performed on the wind turbines in the wind farm. Based on this, the improved spatial association algorithm is used to optimize and sort, and obtain spatial information of typical wind turbine and neighboring domains. Information is reconstructed to improve the efficiency of spatial feature extraction. Then, the reconstructed spatio-temporal three-dimensional information is input into the convolutional memory network to reduce the impact of the lack of information on the prediction accuracy, and spatial feature extraction and multi-step ultra-short-term prediction are performed. Finally, the proposed method verify the prediction accuracy and generalization ability by predicting the wind speed and wind power of different wind farms.
潘超, 李润宇, 蔡国伟, 王典, 张永会. 基于时空关联分解重构的风速超短期预测[J]. 电工技术学报, 2021, 36(22): 4739-4748.
Pan Chao, Li Runyu, Cai Guowei, Wang Dian, Zhang Yonghui. Multi-Step Ultra-Short-Term Wind Speed Prediction Based on Decomposition and Reconstruction of Time-Spatial Correlation. Transactions of China Electrotechnical Society, 2021, 36(22): 4739-4748.
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2021, Vol. 36 
