A Temporal-Spatial Model Based Short-Term Power Load Forecasting Method in COVID-19 Context

Liu, Bowen, Xu, Da, Jiang, Lin ORCID: 0000-0001-6531-2791, Chen, Shuangyin and He, Yong (2022) A Temporal-Spatial Model Based Short-Term Power Load Forecasting Method in COVID-19 Context. FRONTIERS IN ENERGY RESEARCH, 10. 923311-.

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Official URL: http://dx.doi.org/10.3389/fenrg.2022.923311

Abstract

<jats:p>The worldwide coronavirus disease 2019 (COVID-19) pandemic has greatly affected the power system operations as a result of the great changes of socio-economic behaviours. This paper proposes a short-term load forecasting method in COVID-19 context based on temporal-spatial model. In the spatial scale, the cross-domain couplings analysis of multi-factor in COVID-19 dataset is performed by means of copula theory, while COVID-19 time-series data is decomposed <jats:italic>via</jats:italic> variational mode decomposition algorithm into different intrinsic mode functions in the temporal scale. The forecasting values of load demand can then be acquired by combining forecasted IMFs from light Gradient Boosting Machine (LightGBM) algorithm. The performance and superiority of the proposed temporal-spatial forecasting model are evaluated and verified through a comprehensive cross-domain dataset.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	COVID-19, load forecasting, machine learning, multi-factor fusion, time series
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	24 Jun 2022 07:30
Last Modified:	15 Mar 2024 04:52
DOI:	10.3389/fenrg.2022.923311
Open Access URL:	https://www.frontiersin.org/articles/10.3389/fenrg...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3157057