Open AccessOnline cleaning method of power grid energy anomaly data based on improved random forest
Author(s) -
Ke Chen,
Hongkai Wang,
Zhangchi Ying,
Chengxin Zhang,
Jiaqi Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2108/1/012067
Subject(s) - outlier , anomaly detection , random forest , mean squared error , computer science , energy (signal processing) , grid , anomaly (physics) , data mining , statistics , mathematics , artificial intelligence , physics , geometry , condensed matter physics
Aiming at the problem of high root mean square error of traditional power grid energy anomaly data online cleaning, a power grid energy anomaly data online cleaning method based on improved random forest is designed. Firstly, an outlier data recognition model of isolated forest is designed to identify outliers in the data. Secondly, an improved random forest regression model is established to improve the adaptability of random forest to mixed abnormal data, and the data trend is fitted and predicted. Finally, the improved random forest data cleaning method is used to compensate the missing data after removing the mixed abnormal data, so as to clean the abnormal energy data of the power grid. The experimental results show that when the amount of power grid energy anomaly data increases, the cleaning root mean square error of the experimental group is significantly lower than that of the control group. The method in this paper solves the problem of high root-mean-square error in the online cleaning of abnormal data of traditional grid energy.