Day-ahead electricity demand forecasting competition: post-COVID paradigm

Farrokhabadi, M., Browell, J. , Wang, Y., Makonin, S., Su, W. and Zareipour, H. (2022) Day-ahead electricity demand forecasting competition: post-COVID paradigm. IEEE Open Access Journal of Power and Energy, 9, pp. 185-191. (doi: 10.1109/OAJPE.2022.3161101)

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The COVID-19 related shutdowns have made significant impacts on the electric grid operation worldwide. The global electrical demand plummeted around the planet in 2020 continuing into 2021. Moreover, demand shape has been profoundly altered as a result of industry shutdowns, business closures, and people working from home. In view of such massive electric demand changes, energy forecasting systems struggle to provide an accurate demand prediction, exposing operators to technical and financial risks, and further reinforcing the adverse economic impacts of the pandemic. In this context, the “IEEE DataPort Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm" was organized to support the development and dissemination state-of-the-art load forecasting techniques that can mitigate the adverse impact of pandemic-related demand uncertainties. This paper presents the findings of this competition from the technical and organizational perspectives. The competition structure and participation statistics are provided, and the winning methods are summarized. Furthermore, the competition dataset and problem formulation is discussed in detail. Finally, the dataset is published along with this paper for reproducibility and further research.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Authors: Farrokhabadi, M., Browell, J., Wang, Y., Makonin, S., Su, W., and Zareipour, H.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:IEEE Open Access Journal of Power and Energy
ISSN (Online):2687-7910
Published Online:21 March 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in IEEE Open Access Journal of Power and Energy 9: 185-191
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.21227/67vy-bs34

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
315958System-wide Probabilistic Energy ForecastingJethro BrowellEngineering and Physical Sciences Research Council (EPSRC)EP/R023484/2M&S - Statistics