Al-Musaylh, Mohanad S. and Deo, Ravinesh C. 
ORCID: https://orcid.org/0000-0002-2290-6749 and Li, Yan
(2020)
Electrical energy demand forecasting model development and evaluation with maximum overlap discrete wavelet transform-online sequential extreme learning machines algorithms.
Energies, 13 (9):2307.
|
Text (Published Version)
energies-13-02307-v2.pdf Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract
To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional campuses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two campuses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm.
|
Statistics for this ePrint Item |
| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license http://creativecommons.org/licenses/by/4.0/) |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -) |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -) |
| Date Deposited: | 28 Jul 2020 04:33 |
| Last Modified: | 31 Jul 2020 01:52 |
| Uncontrolled Keywords: | energy security; time-series forecasting; predictive model for electricity demand; OS-ELM; wavelet transformation; MODWT; sustainable energy management systems |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling |
| Fields of Research (2020): | 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences |
| Identification Number or DOI: | https://doi.org/10.3390/en13092307 |
| URI: | http://eprints.usq.edu.au/id/eprint/39134 |
Actions (login required)
 |
Archive Repository Staff Only |