Multi‐Step Forecasting for Household Power Consumption

Abstract

AbstractIt is of great importance to build an accurate model for the multi‐step forecasting of household power consumption. In recent years, more and more researchers have focused on adopting hybrid models to execute forecasting due to the irregularity and nonlinearity of power data. However, existing forecasting models usually make predictions directly on original data. This paper introduces secondary decomposition algorithm used to decompose primal data. We use singular spectrum analysis (SSA) to decompose the original series into several subseries, utilize variational mode decomposition (VMD) optimized by whale optimization algorithm to decompose the subseries with the highest frequency into several intrinsic mode functions . Then all subseries obtained from SSA and VMD are fed into long short‐term memory model to get predictions. In order to confirm the validity of proposed model, this paper performs several experimental analyses. The results of experiments show that the proposed model effectively improves the accuracy of forecasting. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.