基于恐惧指数的疫情影响下短期电力负荷预测方法

突如其来的新型冠状病毒肺炎（COVID-19）疫情给电力负荷造成了严重的影响，为了有效应对疫情带来的影响，提高疫情影响下的短期负荷预测精度，提出了一种基于恐惧指数（FI）的疫情影响下短期电力负荷预测方法.利用疫情数据构建FI，与时间信息、历史负荷、气象条件一起作为广义回归神经网络（GRNN）模型的输入变量，用果蝇优化算法（FOA）对GRNN平滑因子进行优化，提高预测结果的准确度和稳定性，使用构建的预测模型进行预测.算例结果表明，该方法能有效提高疫情影响下短期负荷预测的精度，为重大灾难影响下的短期负荷预测提供参考与借鉴. 

Short-Term Power Load Forecasting Method Based on FI under the Impact of Epidemic

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.