This paper presents a study to show the complementarity between solar and wind energy potentials in Benin Republic. Daily wind speed data in the coast of Cotonou city, precisely in Cadjehoun district, has been used to assess wind energy potential. Solar potential is evaluated using spatio temporal daily solar radiation data covering the country. In this research, we have found the locations offering optimal complementarity between solar and wind energy. The complementarity is measured with Pearson correlation coefficient, which is used as objective function to be minimized. The optimization method used is Particle Swarm Optimization (PSO), which has been implemented in Matlab®. We showed that an optimal complementarity is obtained between the coast of Cotonou in the ‘Littoral’ department and the central part of the country in the ‘Collines’ department.

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Africans in general and specially Beninese’s low rate access to electricity requires efforts to set up new electricity production units. To satistfy the needs, it is therefore very important to have a prior knowledge of the electrical load. In this context, knowing the right need for the electrical energy to be extracted from the Beninese network in the long term and in order to better plan its stability and reliability, a forecast of this electrical load is then necessary. The study has used the annual power grid peak demand data from 2001 to 2020 to develop, train and validate the models. The electrical load peaks until 2030 are estimated as the output value. This article evaluates three algorithms of a method used in artificial neural networks (ANN) to predict electricity consumption, which is the Multilayer Perceptron (MLP) with backpropagation. To ensure stable and accurate predictions, an evaluation approach using mean square error (MSE) and correlation coefficient (R) has been used. The results have proved that the data predicted by the Bayesian regulation variant of the Multilayer Perceptron (MLP), is very close to the real data during the training and the learning of these algorithms. The validated model has developed high generalization capabilities with insignificant prediction deviations.