Anomaly detection for carbon trading data in power system based on improved isolation forest method
With the improvement of carbon asset trading mechanism in China, trading has become increasingly active. The rapid increase of carbon asset types and data volume from trading has brought challenges to data aggregation and management of carbon asset operation departments. In order to improve the mana...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-06-01
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| Series: | Diance yu yibiao |
| Subjects: | |
| Online Access: | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20240919002&flag=1&journal_id=dcyyb&year_id=2025 |
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| _version_ | 1849706166184050688 |
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| author | ZHANG Xu WANG Xudong HE Xin WANG Lin FENG Quehe LIU Bin |
| author_facet | ZHANG Xu WANG Xudong HE Xin WANG Lin FENG Quehe LIU Bin |
| author_sort | ZHANG Xu |
| collection | DOAJ |
| description | With the improvement of carbon asset trading mechanism in China, trading has become increasingly active. The rapid increase of carbon asset types and data volume from trading has brought challenges to data aggregation and management of carbon asset operation departments. In order to improve the management efficiency, this paper studies an anomaly detection method for multi-source carbon assets aggregation data based on improved isolation forest algorithm. Types of multi-source carbon assets and the characteristics of aggregation data are discussed. Mutual information-based segmentation of aggregation data is proposed to construct the isolation tree, aiming to reduce data dimension of isolation tree and improve the stability of calculation result, thereby improving the computational efficiency of the algorithm. Experimental results indicate that the proposed improved method has higher efficiency in anomaly detection for carbon assets aggregation data. |
| format | Article |
| id | doaj-art-9cfb625f2c774132ac8e0bdf8a371d4e |
| institution | DOAJ |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-9cfb625f2c774132ac8e0bdf8a371d4e2025-08-20T03:16:15ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-06-0162621822410.19753/j.issn1001-1390.2025.06.0241001-1390(2025)06-0218-07Anomaly detection for carbon trading data in power system based on improved isolation forest methodZHANG Xu0WANG Xudong1HE Xin2WANG Lin3FENG Quehe4LIU Bin5State Grid Tianjin Electric Power Co., Ltd., Tianjin 300010, ChinaState Grid Tianjin Electric Power Co., Ltd., Tianjin 300010, ChinaState Grid Information & Telecommunication Group Co., Ltd., Beijing 102209, ChinaState Grid Tianjin Electric Power Co., Ltd., Tianjin 300010, ChinaState Grid Information & Telecommunication Group Co., Ltd., Beijing 102209, ChinaState Grid Information & Telecommunication Group Co., Ltd., Beijing 102209, ChinaWith the improvement of carbon asset trading mechanism in China, trading has become increasingly active. The rapid increase of carbon asset types and data volume from trading has brought challenges to data aggregation and management of carbon asset operation departments. In order to improve the management efficiency, this paper studies an anomaly detection method for multi-source carbon assets aggregation data based on improved isolation forest algorithm. Types of multi-source carbon assets and the characteristics of aggregation data are discussed. Mutual information-based segmentation of aggregation data is proposed to construct the isolation tree, aiming to reduce data dimension of isolation tree and improve the stability of calculation result, thereby improving the computational efficiency of the algorithm. Experimental results indicate that the proposed improved method has higher efficiency in anomaly detection for carbon assets aggregation data.http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20240919002&flag=1&journal_id=dcyyb&year_id=2025power systemcarbon assetdata aggregationanomaly detection |
| spellingShingle | ZHANG Xu WANG Xudong HE Xin WANG Lin FENG Quehe LIU Bin Anomaly detection for carbon trading data in power system based on improved isolation forest method Diance yu yibiao power system carbon asset data aggregation anomaly detection |
| title | Anomaly detection for carbon trading data in power system based on improved isolation forest method |
| title_full | Anomaly detection for carbon trading data in power system based on improved isolation forest method |
| title_fullStr | Anomaly detection for carbon trading data in power system based on improved isolation forest method |
| title_full_unstemmed | Anomaly detection for carbon trading data in power system based on improved isolation forest method |
| title_short | Anomaly detection for carbon trading data in power system based on improved isolation forest method |
| title_sort | anomaly detection for carbon trading data in power system based on improved isolation forest method |
| topic | power system carbon asset data aggregation anomaly detection |
| url | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20240919002&flag=1&journal_id=dcyyb&year_id=2025 |
| work_keys_str_mv | AT zhangxu anomalydetectionforcarbontradingdatainpowersystembasedonimprovedisolationforestmethod AT wangxudong anomalydetectionforcarbontradingdatainpowersystembasedonimprovedisolationforestmethod AT hexin anomalydetectionforcarbontradingdatainpowersystembasedonimprovedisolationforestmethod AT wanglin anomalydetectionforcarbontradingdatainpowersystembasedonimprovedisolationforestmethod AT fengquehe anomalydetectionforcarbontradingdatainpowersystembasedonimprovedisolationforestmethod AT liubin anomalydetectionforcarbontradingdatainpowersystembasedonimprovedisolationforestmethod |