基于机器学习与疫情关联特征的短期负荷预测

准确的电力负荷预测是电力系统正常运转的重要保障。针对新冠疫情期间负荷需求波动大、历史参考负荷难以建模等问题，提出了一种基于机器学习与静默指数、滚动焦虑指数的短期负荷预测方法。首先，利用谷歌流动性数据和疫情数据构建出静默指数、滚动焦虑指数来量化经济、疫情的发展对电力负荷造成的影响。然后，采用最大信息系数分析疫情期间电力负荷的强相关因素并引入疫情负荷关联特征。最后，将气象数据、历史负荷以及构建的疫情关联特征合并作为预测模型的输入变量，通过多种机器学习模型进行预测算例分析。结果表明，引入疫情关联特征的负荷预测模型能够有效地提高疫情期间负荷预测的准确性。

Short-term load forecasting based on machine learning and epidemic association features

Accurate power load forecasting is an important guarantee for normal operation of a power system. There have been problems of large fluctuations in load demand and difficulty in modeling historical reference load during the COVID-19 outbreak. Thus this paper proposes a short-term load forecasting method based on machine learning, silent index and rolling anxiety index. First, Google mobility data and epidemic data are used to construct the silent index and rolling anxiety index to quantify the impact of the economic and epidemic developments on the power load. Then, the maximal information coefficient is used to analyze the strong correlation factors of power load during the epidemic and introduce epidemic load correlation characteristics. Finally, meteorological data, historical load and the constructed epidemic correlation features are combined as the input variables of the prediction model, and the prediction algorithm is analyzed by multiple machine learning models. The results show that the load forecasting model with the introduction of the epidemic correlation features can effectively improve the accuracy of load forecasting during the epidemic.This work is supported by the National Natural Science Foundation of China (No. 61672032).