Ensemble Multilayer Perceptron Model for Day-ahead Photovoltaic Forecasting

Abstract

World-wide deployment of photovoltaic system requires accurate forecasting concerning of the uncertainty and imprecision of solar radiation. Multilayer perceptron (MLP) is commonly used in day-ahead photovoltaic forecasting, which has excellent performances in convergence speed and a disadvantage of easily causing overfitting. An ensemble model of MLP is proposed in this paper to counteract the overfitting and reduce the variance of a single MLP model. The input of the ensemble model for day-ahead photovoltaic forecasting comprises feature vectors and the 24-hour power generation of the nearest day. The connection coefficients between MLP are defined by the discounting of feature distance, which measures the dissimilarity between input feature vectors. The forecasting results of a PV system in Macau verifies the effectiveness of the proposed ensemble model of MLP for solving day-ahead photovoltaic forecasting problems.