A comparison study based on artificial neural network for assessing PV/T solar energy production

This paper aims to employ and perform a comparison study of PV/T energy data prediction
systems using different ANNs techniques. Several studies focus on photovoltaic thermal (PV/T)
collectors started during the 1970s till now, which aims to increase the photovoltaic efficiency
and produce a hybrid system for electricity and heat production. Locations that have good meteorological
stations for recording solar radiations have been studied to predict solar energy
based on using artificial neural networks (ANNs). Published studies in data sets for the years
2008–2017 were collected from individual countries and evaluated using suitable evaluation
factors like MSE, MAPE, R2, RSME, MBE, and MPE. Furthermore, the best models used to predict
the data of global solar radiation for locations with different latitudes and climates are discussed
and analysed. This study is a guide for the reader and useful for engineers, and researchers
interested in ANNs applied for solar PV/T systems data generation.