期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：199
期号：5
页码：052043
DOI：10.1088/1755-1315/199/5/052043
语种：English
出版社：IOP Publishing
摘要：Precise prediction of photovoltaic (PV) output power can effectively improve the safe and stable operation of power grids. In this paper, the influence of weather types on the output of PV output power is analyzed. Based on extreme learning machine (ELM) neural network, a prediction model of PV power generation output taking into account weather types is established. The grey correlation analysis method is used to process the weather types, and the established ELM neural network prediction model is trained by using the prediction results of the improved equal-dimension grey (IEDG) model, and the trained model is used to predict the output of PV output power. Compared results show that the prediction model presented in this paper can predict the output of each day in different weather types. The results show that the proposed model based weather type is of high accuracy and reliability in predicting the output power of PV.
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