Electricity consumption has been affected due to worldwide lockdown policies against COVID-19. Many countries have pointed out that electricity supply security during the epidemic is critical to ensuring people’s livelihood. Accurate prediction of electricity demand would act a more important role in ensuring energy security for all the countries. Although there have been many studies on electricity forecasting, they did not consider the pandemic, and many works only considered the prediction accuracy and ignored the stability. Driven by the above reasons, it is necessary to develop an electricity consumption prediction model that can be well applied in the pandemic. In this work, a hybrid prediction system is proposed with data processing, modelling, and optimization. An improved complete ensemble empirical mode decomposition with adaptive noise is used for data preprocessing, which overcomes the shortcomings of the original method; a multi-objective optimizer is adopted for ensuring the accuracy and stability; support vector machine is used as the prediction model. Taking daily electricity demand of US as an example, the results prove that the proposed hybrid models are superior to benchmark models in both prediction accuracy and stability. Moreover, selection of input parameters is discussed, and the results indicate that the model considering the daily infections has the highest prediction accuracy and stability, and it is proved that the proposed model has great potential in real-world applications.

Highlights • A hybrid model is developed for predicting daily electricity demand during COVID-19. • The accuracy and stability of the new model are higher than those of benchmark models. • The proposed model also performs well in multi-step prediction. • The model that only considers daily infections has the best prediction performance.