Particle swarm optimized–support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset
Paper
Paper/Presentation Title | Particle swarm optimized–support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset |
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Presentation Type | Paper |
Authors | Al-Musaylh, Mohanad S. (Author), Deo, Ravinesh C. (Author) and Li, Yan (Author) |
Editors | Huang, Q. and Kolhe, M. |
Journal or Proceedings Title | E3S Web of Conferences |
Journal Citation | 64 (08001) |
Number of Pages | 5 |
Year | 2018 |
Place of Publication | Germany |
Digital Object Identifier (DOI) | https://doi.org/10.1051/e3sconf/20186408001 |
Web Address (URL) of Paper | https://www.e3s-conferences.org/articles/e3sconf/abs/2018/39/e3sconf_icpre2018_08001/e3sconf_icpre2018_08001.html |
Conference/Event | 3rd International Conference on Power and Renewable Energy (ICPRE 2018) |
Event Details | 3rd International Conference on Power and Renewable Energy (ICPRE 2018) Event Date 21 to end of 24 Sep 2018 Event Location Berlin, Germany |
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. |
Keywords | daily electricity demand |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q4z62/particle-swarm-optimized-support-vector-regression-hybrid-model-for-daily-horizon-electricity-demand-forecasting-using-climate-dataset
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