Short-Term Photovoltaic Generation Forecasting Based on LVQ-PSO-BP Neural Network and Markov Chain Method

作     者：Xiran Wang Xue Ma Suhua Lou Fei Peng Song Wu 

作者机构：State Key Laboratory of Advanced Electromagnetic Engineering and Technology (College of Electrical and Electronic Engineering Huazhong University of Science and Technology) Wuhan 430074 Hubei Province China State Grid Qinghai Electric Power Company Economic Research Institute Xining 810000 Qinghai Province China 

出 版 物：《Journal of Physics: Conference Series》 

年 卷 期：2019年第1267卷第1期

学科分类：07[理学] 0702[理学-物理学] 

摘      要：With the rapid development of solar photovoltaic generation, the effective prediction of photovoltaic is of great significance to mitigate its impact on power system. According to the analysis of main factors which affect power output of photovoltaic system, a short-term power forecasting model based on back propagation(BP) neutral network and LVQ-PSO-BP neural network and Markov chain method was established. The weather is clustered and distinguished by using learning vector quantization(LVQ) and the particle swarm optimization(PSO) is used to optimize BP neural network weights and thresholds, improving forecasting network training speed. Finally, daily predictive value is corrected by Markov chain method to improve short-term photovoltaic generation forecasting precision. The simulation results indicate that the proposed method can accelerate the speed of searching optimums, improving the classification accuracy of weather types and the precision of the photovoltaic generation output effectively.