Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

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.

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