In order to improve the satisfaction of power users and increase the hit rate of frequent power outage detection,the power outage sensitivity simulation of power station area based on XGBoost integrated machine learning algorithm is studied.It divides the users in the power station area into ordinary users and important users,judges whether they are power outage sensi-tive users,collects historical data of different types of users,takes them as input after data preprocessing and correlation analy-sis,and establishes a power outage sensitivity prediction model in the power station area based on XGBoost integrated machine learning algorithm.It predicts the power outage sensitivity in the power station area through XGBoost algorithm.Bayesian ma-chine learning algorithm is integrated to optimize parameters to obtain the optimal classification threshold.It accurately predicts the power outage sensitivity of users in the power station area.The experimental results show that this method can accurately divide the power outage sensitive user groups,effectively predict the power outage sensitivity of different types of users in the power station area,and the user coverage,and the hit rate of the prediction of sensitive users can reach more than 95％.
关键词
XGBoost算法/集成机器学习/贝叶斯算法/供电台区用户/停电敏感度
Key words
XGBoost algorithm/integrated machine learning/Bayesian algorithm/users in the power station area/power out-age sensitivity
维普
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