Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm

作     者：Li, Ling-Ling Zhao, Xue Tseng, Ming-Lang Tan, Raymond R. 

作者机构：Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300130 Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung 41354 Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan De La Salle Univ Chem Engn Dept Manila Philippines 

出 版 物：《JOURNAL OF CLEANER PRODUCTION》 (清洁器生产杂志)

年 卷 期：2020年第242卷

页      面：118447-000页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 

基　　金：Natural Science Foundation of Hebei Province of China [E2018202282] key project of Tianjin Natural Science Foundation [19JCZDJC32100] 

主　　题：Wind power prediction Support vector machine Differential evolution Improved dragonfly algorithm Prediction accuracy 

摘      要：It is hard to predict wind power with high-precision due to its non-stationary and stochastic nature. The wind power has developed rapidly around the world as a promising renewable energy industry. The uncertainty of wind power brings difficult challenges to the operation of the power system with the integration of wind farms into power grid. Accurate wind power prediction is increasingly important for the stable operation of wind farms and the power grid. This study is combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model. The adaptive learning factor and differential evolution strategy are introduced to improve the performance of traditional dragonfly algorithm. The improved dragonfly algorithm is used to choose the optimal parameters of support vector machine. The effectiveness of the proposed model has been confirmed on the real datasets derived from La Haute Borne wind farm in France. The proposed model has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression. The proposed model is suitable for short-term wind power prediction. (C) 2019 Elsevier Ltd. All rights reserved.