Prediction of Photovoltaic Output Power Based on Match Degree and Entropy Weight Method

Abstract

In this study, an algorithm, which had combined the match degree and entropy weighting method, was proposed to predict the efficiency of the photovoltaic output power. First, the key characteristic quantities were selected by the correlation analyses of output power history data and meteorological data of the photovoltaic power generation system. Second, according to the Euclidean distances between the predicted points and the historical data points, a method for the selections of similar sample points was proposed based on point-to-point theory. Finally, the match degrees between each characteristic quantity of the prediction points and the similar sample points were determined using the Mamdani reasoning method in fuzzy mathematics. The fitting weighting was then solved using the entropy weighting method, which was applied to fit the match degrees of the same characteristic quantities between the similar sample points. Next, the correlation coefficients were utilized as the weighting to fit the match degrees of the different characteristic quantities, resulting in a total match degree. This total match degree was then used to fit the photovoltaic output power of the similar sample points to the prediction points. We had proven that this method could have effective predictions and good adaptabilities in various weather statuses with high accuracy and real-time performance, especially, in the case of sudden weather change.