Modelling and prediction of Future Energy Generation for Jebba Hydro Power Plant Station Using Intelligent Algorithm

Abstract

One of the most important metrics for measuring economic progress is energy production. This is because any nation's social and economic progress depends heavily on its ability to provide a stable and constant supply of electricity. This study is contributing by employing an intelligent algorithm for the energy generation forecast of Jebba hydroelectric power station in Nigeria. A robust programming was performed with MATLAB software to develop an Artificial Neural Network (ANN) model using the feed forward back propagation (FFBP) algorithm. The ANN was employed to surmount the present challenges of lack of reliable forecast tools for power generation that could asset in yielding proper management policies of the power station. Thus, the whole data set which comprises of three hundred and twenty four monthly averages were divided into two categories: 70% training and 30% for validation purposes in order to obtain the optimum topology for network. Base on the results modelling, a Root Mean Square Error (RMSE) value of 5.0430 and 0.02600 were obtained for the classical neural network and the intelligent algorithm for both training and validation data sets with high regression coefficient (R2) of 0.68046. Therefore, the outcome of the results highlight that the proposed model outperformed the classical neural network models, which proves that the model was reliable and could be used for prediction at a 95% confidence level.