EMD-DR models for forecasting electricity load demand

Abstract

Forecasting electricity demand is a vital process since electricity is a hard-to-store resource. To accurately forecast electricity demand, this paper proposes a novel method combining Empirical Mode Decomposition (EMD) and Dynamic Regression namely EMD-DR method. EMD is a technique for detecting non-stationary and nonlinear signal, while Dynamic Regression approach is a method that involves lagged external variables. The EMD-DR method was applied to a half-hourly of electricity demand (kW) and reactive power (var) of Malaysia; where the reactive power data act as exogenous variable for Dynamic Regression method. This paper demonstrates that the proposed EMD-DR model provides a better forecast compared to a single Dynamic Regression model.