Research on Predicting the Short-term Output of Photovoltaic (PV) Based on Extreme Learning Machine Model and Improved Similar Day
Yongmei Jiang, Yang Yang, Qiuxuan Wu, Xiaoni Chi, Jinzi Miao · 2019
In this paper, an extreme learning machine model based on the improved similar day to predict the short-term output of photovoltaic (PV) is presented. The Pearson correlation coefficient is used to analyze the influence of various meteorological factors on the output of photovoltaic power generation, so as to find out the meteorological factors that have a greater impact on photovoltaic output. And then the prediction model is to be established with combining the fuzzy clustering method by improving the similar day selection method. The multi-day photovoltaic output data with the highest correlation about the day to be tested is used to train the extreme learning machine neural network which is then used to predict the PV output of the day to be measured. Finally, the experimental results show that the proposed method has higher prediction accuracy and shorter calculation time than the traditional prediction method, and what's more, the algorithm is simple and the prediction cost is low. It has a wide application value and research space.