南京师范大学学报（工程技术版）2018，Vol.18Issue(2)：19-28,10.DOI:10.3969/j.issn.1672-1292.2018.02.003
基于相似日和交叉熵理论的光伏发电功率组合预测
Photovoltaic Power Generation Combination Forecasting Based on Similar Days and Cross Entropy Theory
摘要
Abstract
In order to further improve the photovoltaic ( PV ) power forecasting accuracy, a short-term combination forecasting model based on similar days and cross entropy theory is proposed. Firstly, the fuzzy C-means clustering method is used to classify the historical samples,and a selection index based on membership degree is proposed to select similar days. Then,the LSSVM,ARMA and BP neural network are used to predict the PV power. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm,and the short-term combination forecasting model of PV power is established. The results show that this method can dynamically identify the information of single methods and obtain appropriate weights. As a result,the forecasting accuracy of PV power can be improved.关键词
光伏发电/组合预测/相似日/隶属度/交叉熵Key words
photovolatic(PV)power generation/combination forecasting/similar days/membership/cross entropy分类
信息技术与安全科学引用本文复制引用
季顺祥,王琦,姚阳,陈佳浩,刘瑾..基于相似日和交叉熵理论的光伏发电功率组合预测[J].南京师范大学学报（工程技术版）,2018,18(2):19-28,10.基金项目
江苏省研究生科研与实践创新计划项目(KYCX17 1078). （KYCX17 1078）
