Particle swarm optimized–support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset

Al-Musaylh, Mohanad S. and Deo, Ravinesh C. and Li, Yan (2018) Particle swarm optimized–support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset. In: 3rd International Conference on Power and Renewable Energy (ICPRE 2018) , 21-24 Sept 2018, Berlin, Germany.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0. This conference was supported by Ministry of Higher Education and Scientific Research in the Government of Iraq for funding the first author’s PhD project.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 10 Dec 2018 05:10
Last Modified: 13 Jun 2019 05:12
Uncontrolled Keywords: daily electricity demand
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: 10.1051/e3sconf/20186408001
URI: http://eprints.usq.edu.au/id/eprint/35220

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