Forecasting the electrical power to be consumed requires planning production on several levels. In this work we used data from the Electric Community of Benin. Temperature, relative humidity, wind speed, normal direct irradiance, precipitation and diffuse radiation are the meteorological variables that made it possible to analyze the forecasts. The objective is to do learning with genetic algorithms, LSTM recurrent neural networks and simple linear regression after a characterization and a correlation study and then to submit the results to performance evaluation criteria. The results of the characterization made it possible to understand that certain variables are significant and influence the consumption of electrical energy. The study of the correlation gives 94% between direct normal irradiance and diffuse irradiance. Both give with the temperature 67% for one and 68% for the other. Regarding modeling, the results are bad with genetic algorithms if we take into account the correlation coefficient (R² = 28.84%), good with simple linear regression (R² = 69.08%) and very interesting for networks of recurrent neurons where we find: MAE = 0.11 MSE = 0.02 MAPE = 18.50% RMSE = 13.09% RRMSE = 18.25% and R² = 96.11%. Given these results, we deduce that short- and long-term memory recurrent neural networks (LSTM) are very well suited to predicting the electrical power consumed on the CEB electrical network.