可再生能源2018，Vol.36Issue(1)：15-21,7.
基于相似日和主成分分析的光伏发电系统短期出力预测
Short-term output power forecast of Photovoltaic power generation system based on similar day and principal component analysis
摘要
Abstract
In view of the fluctuation and the intermittence of t Photovoltaic power,based on principal component analysis (PCA) and particle swarm optimization (PSO) optimization,a short term forecasting method of BP neural network power is proposed.Firstly,this method analyzes the original input data by principal component analysis,then which uses the analysis results as the input data of the BP neural network.The search speed of particle swarm algorithm is slow,but it has a better overall search capability.The traditional BP neural network search speed is relatively fast,but it is prone to local extreme points.Therefore,the combination of the two can make up for both the disadvantages and the failure of the prediction model,so the prediction accuracy of prediction model is improved.The results show that the prediction model is invariable when the type changes,and the forecast error is less than 20％.关键词
光伏发电系统/主成分分析(PCA)/粒子群优化(PSO)算法/BP神经网络Key words
photovoltaic system/principal components analysis (PCA)/particle swarm optimization (PSO)/BP neural network分类
能源科技引用本文复制引用
侯松宝,王侃宏,石凯波,孔力,曹辉..基于相似日和主成分分析的光伏发电系统短期出力预测[J].可再生能源,2018,36(1):15-21,7.基金项目
河北省科技计划项目(15214404D). （15214404D）
