Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy Prices

Abstract

Recently, variational mode decomposition (VMD) has been proposed as an advanced multiresolution technique for signal processing. This study presents a VMD-based generalized regression neural network ensemble learning model to predict California electricity and Brent crude oil prices. Its performance is compared to that of the empirical mode decomposition (EMD) based generalized regression neural network (GRNN) ensemble model. Particle swarm optimization is used to optimize each GRNN initial weight within the ensemble system. Experimental results showed that the VMD-based ensemble outperformed EMD-based ensemble forecasting system in terms of mean absolute error, mean absolute percentage error, and root mean-squared error. It also outperformed the conventional auto-regressive moving average model used for comparison purpose. As a result, the VMD-based GRNN ensemble forecasting paradigm could be a promising methodology for California electricity and Brent crude oil price prediction.