电测与仪表2017，Vol.54Issue(7)：75-80,6.
基于相似日和WNN的光伏发电功率超短期预测模型
A very short-term prediction model for photovoltaic power based on similar days and wavelet neural network
宋人杰 1刘福盛 1马冬梅 2王林2
作者信息
- 1. 东北电力大学信息工程学院,吉林 吉林132012
- 2. 国网吉林供电公司信息通信分公司,吉林 吉林132012
- 折叠
摘要
Abstract
Photovoltaic (PV) generation power prediction has great significance for the stability and security of power grid after the PV grid-connection.In this paper, we propose a very short-term photovoltaic power forecasting methodwhich is based on similar days and wavelet neural networks (WNN).Firstly, the historical weather information from the PV power generation system is utilized to establish meteorological feature vectors, and similar days are found based on computation grey correlation degree.Secondly, the autocorrelation analysis method isused to discover historical output power which has great relation with predicted output power.The historical meteorological data, such as temperature, irradiance and wind speed, are utilized to determine the input factor of this model.Finally, the wavelet neural network (WNN) is utilized to create a forecast model, which is to predict forecasting daily output one by one momentthough the similar historical day data as training sample of WNN.The instance analysis shows that this model has high accuracy, and can provide an effective and feasible way to forecast the very short-term power output of the PV system.关键词
光伏功率预测/相似日/灰色关联/WNN/超短期Key words
photovoltaic power forecast/similar day/grey association/WNN/very short-term分类
信息技术与安全科学引用本文复制引用
宋人杰,刘福盛,马冬梅,王林..基于相似日和WNN的光伏发电功率超短期预测模型[J].电测与仪表,2017,54(7):75-80,6.
