Authors: Yuanyuan Xu; Tianhe Yao; Genke Yang
Addresses: Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China; College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, China ' College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, China ' Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
Abstract: In this paper, we propose an empirical mode decomposition-support vector machine (EMD-SVM) model with error compensation in order to reduce the cumulative error and improve the prediction accuracy of short-term wind speed forecasting. The essential idea behind the proposed approach is that the error of the current prediction is highly correlated with the previous prediction errors, and the forecasted speed should be compensated in terms of the errors incurred from previous predictions. Specifically, we first predict the historical data by the EMD-SVM model so as to obtain the corresponding prediction errors. Then, we establish the error compensation mechanism. Finally, we combine the EMD-SVM model with error compensation to obtain the final prediction results. The error compensation strategy is validated by a series of actual 10 min wind speed data collected from New Zealand. Experimental results demonstrate that the proposed EMD-SVM model with error compensation can be successfully applied to short-term wind speed forecasting, and it has higher accuracy and stronger robustness compared with the method without error compensation.
Keywords: wind speed forecasting; EMD-SVM model; wind speed prediction; error compensation.
International Journal of Information Technology and Management, 2019 Vol.18 No.2/3, pp.171 - 181
Received: 04 Jul 2017
Accepted: 30 Jun 2018
Published online: 10 May 2019 *