Abstract: The sudden COVID-19 epidemic has caused a serious impact on power load. In order to effectively deal with the impact of the epidemic and improve the accuracy of short-term load prediction under the impact of the epidemic, a short-term power load forecasting method based on fear index (FI) under the impact of epidemic was proposed. Firstly, epidemic data was used to construct the FI, together with the time information, historical load and meteorological conditions as the input variables of generalized regression neural network (GRNN) model. And then a fruit fly optimization algorithm (FOA) was used to optimize the GRNN smoothing factor to improve the accuracy and stability of the predicted results. Finally, the model was used to make the prediction. The simulation results show that this method can effectively improve the accuracy of short-term load forecasting under the impact of epidemic and provide reference for short-term load forecasting under the impact of major disasters.