Evaluating Solar Energy Harvesting using Artificial Neural Networks: A Case study in Togo

The work sought to develop a model for the evaluation of solar energy harvesting potentials in Togo using an approach based on artificial neural networks (ANNs) in order to predict the daily irradiation for some areas under climatic conditions. Two types of ANN architecture were evaluated the radial basic function (RBF) and multi-layer perceptron (MLP). The data were collected in 28 cities, and data from of 8 cities were used for the training stage in order to come with the suitable model for the other cities. The results indicated that the ANN-MLP model 8 has provided the best performance and its accuracy was tested. Furthermore, the solar generation potential was evaluated. The sites in the study have exhibited a high potential for solar energy generation.