Energy load forecasting: Bayesian and exponential smoothing hybrid methodology
International Journal of Energy Sector Management
ISSN: 1750-6220
Article publication date: 1 October 2019
Issue publication date: 10 March 2021
Abstract
Purpose
The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques.
Design/methodology/approach
Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R2) indices are used to evaluate forecasts.
Findings
The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R2 above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector.
Originality/value
This is original research.
Keywords
Citation
Khorsheed, E. (2021), "Energy load forecasting: Bayesian and exponential smoothing hybrid methodology", International Journal of Energy Sector Management, Vol. 15 No. 2, pp. 294-308. https://doi.org/10.1108/IJESM-06-2019-0005
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited