电力建设2025，Vol.46Issue(2)：74-87,14.DOI:10.12204/j.issn.1000-7229.2025.02.007
基于用电数据挖掘的企业环保异常识别
Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining
摘要
Abstract
Pollution source enterprises are numerous and widespread.The production and pollution treatment processes of each enterprise vary,a lack of effective and uniform regulatory indicators and early warning systems are concerning.This creates problems,such as difficult supervision,poor real-time performance,and a large workload.This study proposes a method for identifying the environmental anomalies of enterprises based on electricity data mining.First,K-means clustering is used to identify the operating status of the equipment,and a model of the enterprise production line is constructed based on dynamic time-warping distance.Next,continuous and intermittent production lines are classified based on historical data statistics.Furthermore,the Fourier transform is used to identify the production cycle of the production line to establish a model of the environmental conditions suitable for the enterprise.Subsequently,the environmental condition identification method is proposed to identify the environmental conditions for continuous and intermittent production lines.Finally,the proposed method is validated using the monitoring data of actual pollution source enterprises.The electric power intelligent environmental protection platform developed based on the proposed method has been implemented in certain provinces,achieving suitable results.This platform enables the environmental protection department to grasp the situation of enterprise environmental protection,providing both technical means and data support.关键词
用电数据/企业环保/连续型/间歇型/K-means聚类/动态时间规整(DTW)/傅里叶变换Key words
electricity consumption data/enterprise environmental protection/continuous/intermittent/K-means clustering/dynamic time warping(DTW)/Fourier transform分类
信息技术与安全科学引用本文复制引用
陈锦涛,张逸,张良羽,宁志毫..基于用电数据挖掘的企业环保异常识别[J].电力建设,2025,46(2):74-87,14.基金项目
This work is supported by National Natural Science Foundation of China(No.51777035). 国家自然科学基金项目(51777035) （No.51777035）
