Cuckoo search for determining Artificial Neural Network training parameters in modeling operating photovoltaic module temperature

Abstract

Temperature of the photovoltaic (PV) module of a PV system is a significant factor in PV system operation. As the module temperature increases, the voltage of the module decreases. This leads to a reduction in the overall output power. As a result, the modeling of operating PV module temperature is important to investigate the climatic factors which affect the PV module temperature. This paper presents the modeling of operating PV module temperature using an Artificial Neural Network (ANN) with solar irradiance and ambient temperature set as the ANN inputs. Besides that, Cuckoo Search (CS) was employed to determine the optimal number of neurons of the ANN hidden layer, learning rate and momentum rate such that the Mean Absolute Percentage Error (MAPE) of the modeling is minimized. CS was found to outperform an Artificial Bee Colony (ABC) algorithm in optimizing the ANN parameters during training by producing lower MAPE.