Hybrid Short-Term Wind Speed Prediction Model by COA-SVR Based on Recursive Quantitative Analysis
PAN Chao1, TAN Qide1, CAI Guowei1, ZHANG Zixin2
1. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, Jilin Province, China; 2. Economic and Technological Research Institute, State Grid Liaoning Electric Power Company,Shenyang 110015, Liaoning Province, China
Abstract: As scale of wind power integration continuously increases, its impact on safe and stable operation of power systems becomes increasingly serious. Aiming at randomness and uncertainty of wind speed fluctuation, a recursive quantitative analysis method is proposed in this paper. Firstly, predictability of wind speed series is quantitatively analyzed according to joint index combined by recurrent rate and determinism. The wind speed series is reconstructed using the parameters optimized with joint index. And the best input sets of prediction model are obtained by embedding dimension and delay time. Then the support vector regression (SVR) model optimized with cuckoo optimization algorithm (COA) is used to predict wind speed. According to actual wind speed data, accurateness and effectiveness of the hybrid prediction model are verified by comparing prediction results of different algorithms. Finally, hypothesis test is performed to evaluate generalization ability of the hybrid prediction model.
潘超, 谭启德, 蔡国伟, 张子信. 基于递归量化分析的COA-SVR短期风速混合预测模型[J]. 电网技术, 2018, 42(8): 2373-2381.
PAN Chao, TAN Qide, CAI Guowei, ZHANG Zixin. Hybrid Short-Term Wind Speed Prediction Model by COA-SVR Based on Recursive Quantitative Analysis. POWER SYSTEM TECHNOLOGY, 2018, 42(8): 2373-2381.
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