Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

Mohanad S. Al-Musaylh, Ravinesh C. Deo and Yan Li
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Mohanad S. Al-Musaylh: School of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Ravinesh C. Deo: School of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Yan Li: School of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia

Energies, 2020, vol. 13, issue 9, 1-19

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.

Keywords: energy security; time-series forecasting; predictive model for electricity demand; OS-ELM; wavelet transformation; MODWT; sustainable energy management systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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