A Temporal-Spatial Model Based Short-Term Power Load Forecasting Method in COVID-19 Context - Liu, Bowen; Xu, Da; Jiang, Lin; Chen, Shuangyin; He, Yong (Frontiers in Energy Research, 2022)
The worldwide coronavirus disease 2019 (COVID-19) pandemic has greatly affected the power sys-tem 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 are decomposed via variational mode decomposition (VMD) algorithm into different intrinsic mode functions (IMFs) 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.