Electrical Load Forecasting Using Artificial Neural Network: The Case Study of the Grid Inter-Connected Network of Benin Electricity Community (CEB)

Abstract

The low rate of electrification seems challenging in many West African countries and many strategies are underway to improve upon. In this regard, the target of achieving the universal access and services calls for a stable and reliable electrical network. Forecasting of electrical load on a connected grid network is very delicate and requires tremendous task from the utilities (billing Company). It aims at looking at if the offered energy is sufficient or below satisfactory in order to add or inject more compensating energy units into the system. Consequently, the short term forecasting is used in evaluating the risk of electricity shortage and reducing the advent of load shedding in an emerging economy alike the energetic Body of Benin comprising Togo and Benin. This paper evaluates two methods used in Artificial Neural Networks (ANN) for the prediction of electricity consumption. These methods are the Multilayer Perceptron (MLP) and the Radial Basic Function (RBF). Many topologies of the hidden layers’ configuration for the learning stages were considered in cross comparison against real data obtained from the grid interconnected Network of Benin. The results have proven that the predicted data are very close to the real data while using these algorithms.