电力系统自动化2012，Vol.36Issue(19)：37-41,5.
基于灰色神经网络组合模型的光伏短期出力预测
Short-term Output Power Forecast of Photovoltaic Based on a Grey and Neural Network Hybrid Model
摘要
Abstract
The output power of photovoltaic(PV) is uncertain.In order to mitigate the negative impacts of the uncertainty on power grid,a grey and neural network(grey-NN) hybrid model is proposed to forecast the short-term output power of PV.This hybrid forecasting method combines traditional direct and indirect forecasting methods.It considers the main factors that influences the PV output power and builds the hourly power grey models by choosing proper samples which have the similar weather condition with the prediction day.Then a neural network model is set up to obtain the final predicted power.The input data of the neural network model are output of grey models plus temperature.This paper uses practical output power data of PV to evaluate three different models of grey,neural network and grey-NN.The evaluation results show that the grey-NN model can predict the short-term output power of PV more precisely and has a potential value in practical applications.关键词
灰色模型/神经网络模型/光伏发电/功率预测/短期预测Key words
grey model/neural network model/photovoltaic generation/power forecasting/short-term forecasting分类
信息技术与安全科学引用本文复制引用
王守相,张娜..基于灰色神经网络组合模型的光伏短期出力预测[J].电力系统自动化,2012,36(19):37-41,5.基金项目
国家重点基础研究发展计划（973计划）资助项目 （）
国家自然科学基金资助项目 （）
国家高技术研究发展计划（863计划）资助项目 （）
