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

Frontiers in Energy Research

Lin Jiang¹  Da Xu²  Bowen Liu²  Yong He²  Shuangyin Chen⁴ 

[1] Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China;Institute of New Energy, Wuhan, China;School of Automation, China University of Geosciences, Wuhan, China;School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China;

关键词: load forecasting;    time series;    multi-factor fusion;    machine learning;    COVID-19;   

DOI  :  10.3389/fenrg.2022.923311

来源: DOAJ

【 摘 要 】

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 via 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.

【 授权许可】

Unknown