Evaluation and Comparison of FFB, CFB and MLP Artificial Neural Networks for Identification of Potential Sites for Construction of Photovoltaic Solar Power Plants in East Azarbaijan Province

Abstract

Due to the geographical location of Iran and having 300 sunny days, the use of solar energy in both large and small sectors contributes to sustainable energy supply.In this study, we have tried to combine the existing methods for location namely the use of spatial decision-making systems and GIS, to use new methods such as artificial neural networks to identify potential areas for the construction of solar photovoltaic (PV) power plants in East Azarbaijan province. To accomplish this, based on the criteria for the construction and location of photovoltaic solar power plants, environmental factors include solar radiation, precipitation, sundial and temperature as climate criteria, elevation and slope as physical and land use criteria, distance from roads and distance from Cities are considered as economic criteria. Based on these criteria, training data was obtained through ANP, and along with this data, and LM training algorithm was performed to train FFB, CFB, and MLP networks.Based on the MSE and RMSE evaluation criteria, the CFB network with the structure of 9,6,1 was selected as the most appropriate network and the results were obtained from this network. After preparing the final map, it was determined that solar photovoltaic power plants could be built in the province.