一种基于KMeans与Random Forest的异常温升捕捉方法
A Method for Capturing Abnormal Temperature Rise Based on KMeans and Random Forest
汪海良1
作者信息
- 1. 上海建筑设计研究院有限公司,上海 200041
- 折叠
摘要
针对线路老化、线路过载的火灾频发问题,分析了线路老化、线路过载与异常温升之间的关联性,以电流值、线缆温度作为输入,利用KMeans聚类算法划分可能存在异常温升的区间,通过Random Forest算法识别线路过载问题,可以提前通知用户整改线路,预防火灾的发生.
Abstract
In response to the frequent occurrence of fire caused by line aging and overload,this article analyzes the correlation between line aging,line overload,and abnormal temperature rise.Using current values and cable temperature as inputs,KMeans clustering algorithm is used to divide the possible intervals of abnormal temperature rise.Random Forest algorithm is used to identify line overload problems,and users can be notified in advance to rectify the line and prevent the occurrence of fire.
关键词
线路过载/异常温升/Random/Forest/KMeansKey words
line overload/abnormal temperature rise/Random Forest/KMeans引用本文复制引用
出版年
2024
NETL
NSTL
万方数据