Partial Evolutionary ANN for Output Prediction of a Grid-Connected Photovoltaic System

Abstract

This paper presents the Evolutionary Programming (EP) based technique to optimize the architecture and training parameters of a one-hidden layer backpropagation Artificial Neural Network (ANN) model for the prediction of total AC power output from a grid connected photovoltaic system. A partial Evolutionary Programming-ANN (EPANN) model has been developed for the prediction. It utilizes solar radiation, wind speed and ambient temperature as its inputs while the output is the total AC power produced from the grid connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for training. The results obtained from the EPANN have been compared with the results from a classical ANN with similar input and output settings. It is observed that the prediction of total AC power output from a grid connected PV system could be accelerated and simplified using the partial evolutionary ANN model. Index Terms—Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), and Photovoltaic (PV).