A hybrid bat Algorithm Artificial Neural Network for grid-connected photovoltaic system output prediction

This research have been conducted to predict the output power of grid-connected photovoltaic (GCPV) system using hybrid Bat Algorithm-Artificial Neural Network (BA-ANN) in this paper. In this project, ANN utilized data from GCPV database includes Solar Irradiance (SI), Ambient Temperature (AT) and Module Temperature (MT) as the inputs and apply output power as a single output. More importantly, bat algorithm optimization was apply to minimize Root Mean Square Error (RMSE) by optimized the number of neurons in the hidden layer, learning rate and momentum rate. After training steps, testing will take a part for affirm the ANN training. The results obtained have been compared with the results from Evolutionary Programming-Artificial Neural Network (EP-ANN) with the similar input and output configurations. It is observed that result for BA-ANN had performed more than EP-ANN in term of producing lower RMSE. Besides that, optimal learning algorithm, time taken, and population were also take part in this research.