A new class of MODWT-SVM-DE hybrid model emphasizing on simplification structure in data pre-processing: A case study of annual electricity consumptions

Abstract

In recent years, electricity crisis still becomes noticeable in some countries due to a widening gap between demand and supply. Consequently, the future demand plays a significant role in efficient management and utilization of electricity. Pertaining to efficient supply handling to increase the power system reliability, an electricity demand forecasting is one of the most crucial tools. The forecasting technique is used by decision makers all over the world to predict the future demand as key information for a proper policy. In this research, the hybrid model consists of maximal overlap discrete wavelet transform (MODWT), support vector machine (SVM), and differential evolution (DE) optimization emphasizing on simplifying the complex structure in data pre-processing is proposed to forecast the thirty-two annual electricity consumptions and is compared with traditional forecasting models, hybrid model of MODWT and SVM, and combined model of SVM and DE optimization based on mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) measures as well as Friedman test and post hoc test. The empirical results indicate that the proposed model outperforms other forecasting models and provides more accurate forecasts than other candidate models at 0.05 significance levels and the nearly highest precision. Consequently, the proposed model is able to reduce the limitations of individual models regarding annual electricity consumptions and can be used as a promising tool in order to forecast annual electricity consumptions as well.