期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：11
页码：273-284
出版社：SERSC
摘要：raditional wind power prediction is only applicable to a single wind farm. Aim at this isolated prediction method. In this paper, combing with the information sharing and Interconnection mechanism of energy Internet, we propose an output power prediction method for multiple wind farms based on DBPSO-LSSVM model. Firstly, collect SCADA data of multiple wind farms in different areas. Secondly, delete outliers of different farms based on DBSCAN algorithm and select multiple wind fields training samples. And searching the optimal input parameters of LSSVM based on particle swarm algorithm to construct every wind farm model. Thirdly, predict multiple wind fields power combined with numerical weather prediction system. The method we propose can be used to make the scheduling plan in advance to solve a large number of abandoned wind power rationing problem every year. In experiment, the method we propose has the lowest error rate compares to LSSVM and BP-neural network. It’s more suitable to predict wind fields in different areas.
关键词：Multi-wind farm power prediction; Energy Internet; Least square support ;vector machine; Particle swarm; Wind power utilization
