Neural network integrated with regression methods to forecast electrical load

Since electrical load forecasting plays significant role in effective and economic operation of power utilities, it has long been of interest to researchers and academics. This paper combined artificial neural network and regression modelling methods to predict electrical load. We propose an approach for specific day, week and/or month load forecasting for electrical companies taking into account the historical load. Therefore, a modified technique, based on artificial neural network (ANN) combined with linear regression, is applied on the KSA electrical network dependent on its historical data to predict the electrical load demand forecasting up to year 2020. This technique was compared with extrapolation of trend curves as a traditional method (Linear regression models). Application results show that the proposed method is feasible and effective. The application of neural networks prediction shows the capability and the efficiently of the proposed techniques to obtain the predicting load demand up to year 2020.