基于改进EMD及Elman算法的短期光伏功率预测研究
摘要：本文提出一种基于改进EMD算法及Elman算法相结合的光伏功率预测方法。首先对历史数据根据辐照时长及辐照强度进行聚类分析,确定待预测日的所属类别及对应的辐照强度待预测时段;其次根据主环境特征量在待预测日所属类别中构建同类型日时间序列,利用改进EMD算法对同类型相似日时间序列进行中值滤波,并按波动程度进行模态分解,同类型模态划归一类,最后采用Elman算法对各模态类进行辐照强度预测,进而得到光伏逐时发电功率值。该方法旨在提高弱辐照情况下对辐照强度预测精准度,经验证,该方法适应了不同类型日的辐照强度预测,能够在一定程度上提高预测精度。
Abstract:A short term power forecasting model of photovoltaic system based on the improved EMD and Elman neural network is proposed. First of all, Historical data is dealt by cluster analysis according to time period and intensity of radiation, and the category of predicted day and relative predicting period of radiation intensity are determined. Then hourly sequences of similar days from the category of the predicted day are built according to principal environmental factors of solar radiation, and the median filtering is carried out using improved EMD algorithm, which is decomposed into different channels according to the fluctuation degree, and channels with similar characteristic are classified as a group. In the end, the radiation intensity of each mode is predicted by using Elman model, and the hourly power output is obtained. This method aims to increase the power forecasting accuracy of photovoltaic system under weak solar radiation circumstance, which can predict the radiation intensity of different days and improve predicting accuracy in certain degree.
标题：基于改进EMD及Elman算法的短期光伏功率预测研究
英文标题：A Short\|term Power Forecasting Model of Photovoltaic System Based on Improved EMD and Elman Neural Network
作者：徐敏姣, 徐青山, 袁晓冬,
关键词：同类型相似日, 中值滤波, EMD分解, Elman模型, 辐照强度,
发表日期：2016-06-14
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