Short-term Load Forecasting based on Wavelet Approach

This paper develops a novel short-term load forecasting technique to predict the demanding power for the next hour. In this study, a linear equation-error Auto Regressive Auto Regressive Moving Average Exogenous (ARARMAX) model is trained to specify power consumption as a function of a few past hours. The parameters of the candidate mathematical model are estimated by using two least squares-based iterative algorithms. The main difference with these algorithms is the total number of past data involved in the modeling. Whereas practical data are always subject to noise and un-accurate measuring, a wavelet de-noising technique is utilized to reduce the effect of noise on forecasting which leads to more precise predictions. The superiority of the proposed approach is validated by utilizing practical data from a power utility in Canada in January 1995. The first three days’ data are utilized to train the selected model and the fourth-day data are dedicated to test the prediction of the provided model. The L2 and L∞ norms error and MAPE, MAE, and RMSE are selected as criteria to show the merits of the proposed approach.