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.

由于社会经济行为的巨大变化，2019 年全球冠状病毒病 (COVID-19) 大流行极大地影响了电力系统的运行。本文提出了一种基于时空模型的 COVID-19 环境下的短期负荷预测方法。在空间尺度上，利用 copula 理论对 COVID-19 数据集中的多因素进行跨域耦合分析，同时对 COVID-19 时间序列数据进行分解通过变分模态分解算法在时间尺度上分解成不同的固有模态函数。然后可以通过结合来自轻型梯度提升机 (LightGBM) 算法的预测 IMF 来获得负载需求的预测值。通过全面的跨域数据集评估和验证所提出的时空预测模型的性能和优越性。