电测与仪表2017，Vol.54Issue(7)：28-33,6.
基于遗传算法小波神经网络的光伏微网发电预测
Application for photovoltaic micro-grid power forecasting usingimproved wavelet neural networks-based on GA
刘爱国 1黄泽平 1薛云涛 1汪硕承1
作者信息
- 1. 南昌大学 信息工程学院, 南昌 330031
- 折叠
摘要
Abstract
It is important for the energy conservation and emissions reduction to accurately predicate the power of photovoltaic micro-grid in a certain period of time in the future.In this paper, by comparing the power generation and meteorological history data, analyzes the factors such as weather, solar radiation and temperature in the photovoltaic power generation prediction, meanwhile, based on the global optimization searching performance of the genetic algorithm and the time-frequency localization of the wavelet neural networks, micro-grid photovoltaic power generation forecasting model has been established.Through case analysis, the results show that wavelet neural network based on genetic algorithm has better learning ability and generalization ability.And in the aspect of micro-grid photovoltaic power, the forecasting data as the network input is more valuable in improving the prediction precision of the model.关键词
光伏微网/光伏功率预测/气象因子/遗传算法/小波神经网络Key words
photovoltatic micro-grid/photovoltaic(PV) power forecast/meteorological factor/genetic algorithm/wavelet neural networks分类
信息技术与安全科学引用本文复制引用
刘爱国,黄泽平,薛云涛,汪硕承..基于遗传算法小波神经网络的光伏微网发电预测[J].电测与仪表,2017,54(7):28-33,6.
