Optimizing three-layer neural network model for grid-connected photovoltaic system output prediction

This paper presents the evolutionary neural network (ENN) model for the prediction of output from a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. The ENN model had been developed using evolutionary programming (EP) through the optimization of the number of nodes in the hidden layer, the learning rate and the momentum rate. The ENN model employs solar irradiance and ambient temperature as its inputs while the sole output is the kilowatt-hour energy output produced from the grid connected PV system. On the other hand, the objective function of the ENN is to maximize the correlation coefficient, R of the prediction task. In this study, the optimal pool population size in the ENN algorithm was investigated. Apart from that, the maximum average correlation coefficient obtained for the ENN training is 0.9942. Besides that, the testing process produced sufficiently high correlation coefficient value of 0.9922.