重庆大学学报2017，Vol.40Issue(4)：54-61,8.DOI:10.11835/j.issn.1000-582X.2017.04.007
基于无迹卡尔曼滤波神经网络的光伏发电预测
Photovoltaic power forecasting based on unscented Kalman filtering neural network
摘要
Abstract
As the existing photovoltaic power prediction methods have low robustness under different weather conditions,we proposed a new method for the prediction of photovoltaic power system based on the unscented Kalman filtering (UKF) neural network.The method uses the unscented Kalman filter to update the weight of the neural network model in real time,and establishes two independent dual-inputsingle-output models with taking DC voltage and current as input and active power and reactive power as output.The experimental results indicate that the proposed UKF neural network model can accurately forecast the photovoltaic power,the best fit of active and reactive power are 97.3％ and 94.2％ respectively,and the method is robust to weather conditions.关键词
光伏发电预测/无迹卡尔曼滤波/神经网路/最佳拟合度Key words
photovoltaic power forecasting/unscented Kalman filter/neural network/optimal degree of fitting分类
信息技术与安全科学引用本文复制引用
李春来,张海宁,杨立滨,杨军,王平..基于无迹卡尔曼滤波神经网络的光伏发电预测[J].重庆大学学报,2017,40(4):54-61,8.基金项目
青海省光伏发电并网技术重点实验室项目(2014-Z-Y34A).Supported by Project of Key Laboratory of Grid-Connected Photovoltaic Technology of Electric Power Research Institute of Qinghai Power Grid Corportation(2014-Z-Y34A). （2014-Z-Y34A）
