Performance analysis of three ANN models using improved fast evolutionary programming for power output prediction in grid-connected photovoltaic system

This paper presents an assessment of three ANN models using hybrid Improved Fast Evolutionary Programming IFEP-ANN techniques for solving single objective optimization problem. In this study, multi-layer feed forward ANN models for the prediction of the total AC power output from a grid-connected PV system has been chosen. The three models were developed based on different sets of ANN inputs. It utilizes solar radiation, ambient temperature and module temperature as its inputs. However, all three models utilize similar output, which is total AC power produced from the grid-connected PV system. The mixtures of Gaussian and Cauchy are used during the mutation process in the EP technique. The best predictive model was selected based on the lowest root mean square error (RMSE) and higher regression, R. Besides, the comparison between classical ANN (without evolutionary programming) and hybrid IFEP-ANN was compared to determine which model performs better for single-objective optimization. The IFEP-ANN models showed the best in having the lowest RMSE and significantly better than ANN in terms of highest regression, R