A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting

作     者：Niu, Xinsong Wang, Jiyang 

作者机构：Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China 

出 版 物：《APPLIED ENERGY》 (实用能源)

年 卷 期：2019年第241卷

页      面：519-539页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：National Natural Science Foundation of China 

主　　题：Wind speed forecasting Combined model Artificial intelligence Data preprocessing strategy Multi-objective optimization algorithm 

摘      要：Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting, a large number of prediction methods have been proposed. However, existing prediction models ignore the role of data preprocessing and are susceptible to various limitations of the single individual model that can lead to low prediction accuracy. In this study, a developed combined model is proposed, including complete ensemble empirical mode decomposition with adaptive noise-a multi-objective grasshopper optimization algorithm based on a no-negative constraint theory-and several single models, including four neural network models and a linear model, to achieve accurate prediction results. The novel combined model considers the linear and nonlinear characteristics of the sequence, successfully overcomes the limitations of the single model, and obtains accurate and stable prediction results. In order to test the performance of combined model, the wind speed sequence of a wind farm from China is used for experiments and discussions. The results of the experiments and discussions show that the novel combined model has better forecasting performance than traditional prediction models.