Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining
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 per...
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Editorial Department of Electric Power Construction
2025-02-01
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| Series: | Dianli jianshe |
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| Online Access: | https://www.cepc.com.cn/fileup/1000-7229/PDF/1738997704319-2096814231.pdf |
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| author | CHEN Jintao, ZHANG Yi, ZHANG Liangyu, NING Zhihao |
| author_facet | CHEN Jintao, ZHANG Yi, ZHANG Liangyu, NING Zhihao |
| author_sort | CHEN Jintao, ZHANG Yi, ZHANG Liangyu, NING Zhihao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-85912c1f6916460992e371c554d18d02 |
| institution | Kabale University |
| issn | 1000-7229 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Editorial Department of Electric Power Construction |
| record_format | Article |
| series | Dianli jianshe |
| spelling | doaj-art-85912c1f6916460992e371c554d18d022025-02-10T09:54:54ZzhoEditorial Department of Electric Power ConstructionDianli jianshe1000-72292025-02-01462748710.12204/j.issn.1000-7229.2025.02.007Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data MiningCHEN Jintao, ZHANG Yi, ZHANG Liangyu, NING Zhihao01. College of Electrical Engineering and Automation， Fuzhou University， Fuzhou 350108， China;2. Electric Power Science Research Institute， State Grid Hunan Electric Power Co.， Ltd.， Changsha 410031， ChinaPollution 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.https://www.cepc.com.cn/fileup/1000-7229/PDF/1738997704319-2096814231.pdfelectricity consumption data|enterprise environmental protection|continuous|intermittent|k-means clustering|dynamic time warping （dtw）|fourier transform |
| spellingShingle | CHEN Jintao, ZHANG Yi, ZHANG Liangyu, NING Zhihao Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining Dianli jianshe electricity consumption data|enterprise environmental protection|continuous|intermittent|k-means clustering|dynamic time warping （dtw）|fourier transform |
| title | Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining |
| title_full | Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining |
| title_fullStr | Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining |
| title_full_unstemmed | Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining |
| title_short | Identification of Environmental Anomalies in Enterprises Based on Electricity Consumption Data Mining |
| title_sort | identification of environmental anomalies in enterprises based on electricity consumption data mining |
| topic | electricity consumption data|enterprise environmental protection|continuous|intermittent|k-means clustering|dynamic time warping （dtw）|fourier transform |
| url | https://www.cepc.com.cn/fileup/1000-7229/PDF/1738997704319-2096814231.pdf |
| work_keys_str_mv | AT chenjintaozhangyizhangliangyuningzhihao identificationofenvironmentalanomaliesinenterprisesbasedonelectricityconsumptiondatamining |