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.
Al-Musaylh, Mohanad S. and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Adamowski, Jan F. and Li, Yan
(2019)
Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia.
Renewable and Sustainable Energy Reviews, 113:109293.
ISSN 1364-0321
Al-Musaylh, Mohanad S. and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Adamowski, Jan F. and Li, Yan
(2018)
Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia.
Advanced Engineering Informatics, 35 (C).
pp. 1-16.
ISSN 1474-0346
Al-Musaylh, Mohanad S. and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Li, Yan and Adamowski, Jan F.
(2018)
Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting.
Applied Energy, 217.
pp. 422-439.
ISSN 0306-2619
Al-Musaylh, Mohanad S. and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 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.