A hybrid approach to model and forecast the electricity consumption by NeuroWavelet and ARIMAX-GARCH models

Today, electrical energy plays a major role in production and consumption and is of special importance in economic decision-making process. Being aware of electrical energy demand for each period is necessary to correct planning. Therefore, the forecasting of electricity consumption is important among several economic sections. Besides the traditional models, in this paper, we offer a hybrid forecast approach that combines the adaptive wavelet neural network with the ARIMA-GARCH family models and uses the effective exogenous variables on electricity consumption. Based on this approach, two hybrid models are proposed. To assess the ability of the proposed models, we forecasted the daily electricity consumption by the hybrid and benchmark models for 60 days ahead in two separate seasons (summer and winter). The empirical results showed that the proposed models have more prediction accuracy compared with the other benchmark forecast models including neural network, adaptive wavelet neural network, and ARIMAX-GARCH family models.