Enhancing electric load forecasting of ARIMA and ANN using adaptive Fourier series

Since the early 20th century, Electricity is a necessity and essential to modern life. Producing and delivering electricity to 7.4 billion people is one of the most complex challenges in this decade. It is essential to forecast the energy load at different intervals. Several methods have been developed to carry out those predictions. Examples of such methods are ARIMA, regression and artificial Neural Network (ANN). This work investigates the usage of adaptive Fourier series in enhancing the prediction accuracy of ARIMA and ANN models. Those models along with their enhanced ones are applied to forecasting hourly Peak load data of The Electric Reliability Council of Texas (ERCOT). The experimental results demonstrate that using the adaptive Fourier series enhances the prediction power of the ARIMA and ANN models. The RMSE, MAE and MAPE are enhanced by increasing the number of harmonics required for a specific seasonal component.