A Hybrid Model for Short-Term Wind Speed Forecasting Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machines
Wang He1, Hu Zhijian1, Zhang Yihui2, Li Chen1, Yang Nan1, Wang Zhansheng3
1.Wuhan University, Wuhan 430072 China; 2.Guangxi Electric Power Research Institute, Nanning 530000 China; 3.Xinxiang Power Supply Company, Xinxiang 453002 China
Abstract：This paper introduces a combination forecasting model which is based on ensemble empirical mode decomposition and least squares support vector machines(LSSVM) into forecasting short-term wind speed, in the view of excavating the nonstationarity and nonlinearity of wind series. Firstly, wind series are decomposed into a group of relatively stable subsequence by ensemble empirical mode decomposition to reduce mutual influences among diverse trend information. Secondly, build forecasting models respectively for each subsequence adopting least squares support vector machines. Adaptive disturbance particle swarm optimization and model learning feedback mechanism is used to jointly optimize the dimension of the learning sample input and the hyper parameters of LSSVM forecasting model to lower the predicting risk. Finally, the predicting results of each subsequence are superposed to obtain wind speed forecasting results. The case study shows that the proposed combination forecasting model is able to excavate wind series features effectively and has relatively high predicting accuracy.
王贺, 胡志坚, 张翌晖, 李晨, 杨楠, 王战胜. 基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J]. 电工技术学报, 2014, 29(4): 237-245.
Wang He, Hu Zhijian, Zhang Yihui, Li Chen, Yang Nan, Wang Zhansheng. A Hybrid Model for Short-Term Wind Speed Forecasting Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machines. Transactions of China Electrotechnical Society, 2014, 29(4): 237-245.
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2014, Vol. 29 
