Particle Swarm Optimized–Support Vector Regression Hybrid Model for Daily Horizon Electricity Demand Forecasting Using Climate Dataset

¹ School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, QLD 4350, Australia
² Management Technical College, Southern Technical University, Basrah, Iraq

Abstract

This paper has adopted six daily climate variables for the eleven major locations, and heavily populated areas in Queensland, Australia obtained from Scientific Information for Land Owners (SILO) to forecast the daily electricity demand (G) obtained from the Australian Energy Market Operator (AEMO). Optimal data-driven technique based on a support vector regression (SVR) model was applied in this study for the G forecasting, where the model’s parameters were selected using a particle swarm optimization (PSO) algorithm. The performance of PSO–SVR was compared with multivariate adaptive regression spline (MARS) and the traditional model of SVR. The results showed that the PSO–SVR model outperformed MARS and SVR.