Short Term Photovoltaic Power Generation Forecasting Using RBF Neural Network
The short-term photovoltaic power generation forecasting is of great significance for the power system and energy management system(EMS).In this paper,the short-term forecasting model of PV generation power based on the RBF neural network is proposed,which forecast the power of PV generation system for the next 24 hours.Factors of position,environment,and inner performance of the system are fully considered.A novel prediction strategy combined with mechanism model is used,and modulations of parameters are executed according to online training of neural network.Experimental results prove that the proposed model reduces the deviation between the predict power and the actual power significantly,and can achieve fast and accurate prediction even the amount of number is very small.
Photovoltaic Power Generation Forecast Neural Network Mechanism Model Online Training
ZhiYong LI YunLei ZHOU Cheng CHENG Yao LI KeXing LAI
School of Information Science and Engineering,Central South University,Changsha,Hunan,410083,China
国际会议
长沙
英文
2758-2763
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）