Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment
Identifying electricity price anomalies and exploring the underlying reasons in such a complex market environment, especially with incomplete data, is a key issue for promoting the orderly operation of power market and ensuring the reasonable interests of power customers. Therefore, a method is esta...
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| Format: | Article |
| Language: | Chinese |
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Editorial Office of Journal of Shanghai Jiao Tong University
2025-07-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-7-995.shtml |
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| author | ZHU Feng, SHAN Chao, WU Ning, CAI Qixin, ZHU Yunan, LIU Yunpeng, ZUO Qiang |
| author_facet | ZHU Feng, SHAN Chao, WU Ning, CAI Qixin, ZHU Yunan, LIU Yunpeng, ZUO Qiang |
| author_sort | ZHU Feng, SHAN Chao, WU Ning, CAI Qixin, ZHU Yunan, LIU Yunpeng, ZUO Qiang |
| collection | DOAJ |
| description | Identifying electricity price anomalies and exploring the underlying reasons in such a complex market environment, especially with incomplete data, is a key issue for promoting the orderly operation of power market and ensuring the reasonable interests of power customers. Therefore, a method is established for feature extraction and anomaly identification of electricity prices for power customers. First, an electricity price feature vector is constructed, and its dimensionality is reduced using a spectral clustering algorithm. Then, typical electricity price characteristics are extracted as the basic standard for determining price anomalies. Next, the similarity between each power customer and typical electricity price characteristics is calculated. Finally, electricity price anomalies are identified in two stages. The causes of anomalies are initially and rapidly identified based on electricity consumption and trading behavior, and then further identified in-depth. Case analysis shows that this method can quickly and effectively extract typical electricity price features and identify anomalies. The reasons behind these anomalies are further analyzed from both electricity consumption and trading behaviors, and corresponding improvement measures are proposed. |
| format | Article |
| id | doaj-art-bcb6db04887f4cd7a8411f5d9221d2a0 |
| institution | Matheson Library |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-bcb6db04887f4cd7a8411f5d9221d2a02025-07-22T09:10:24ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-07-01597995100610.16183/j.cnki.jsjtu.2023.448Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market EnviromentZHU Feng, SHAN Chao, WU Ning, CAI Qixin, ZHU Yunan, LIU Yunpeng, ZUO Qiang01. Marketing Service Center of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, China;2. State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210019, ChinaIdentifying electricity price anomalies and exploring the underlying reasons in such a complex market environment, especially with incomplete data, is a key issue for promoting the orderly operation of power market and ensuring the reasonable interests of power customers. Therefore, a method is established for feature extraction and anomaly identification of electricity prices for power customers. First, an electricity price feature vector is constructed, and its dimensionality is reduced using a spectral clustering algorithm. Then, typical electricity price characteristics are extracted as the basic standard for determining price anomalies. Next, the similarity between each power customer and typical electricity price characteristics is calculated. Finally, electricity price anomalies are identified in two stages. The causes of anomalies are initially and rapidly identified based on electricity consumption and trading behavior, and then further identified in-depth. Case analysis shows that this method can quickly and effectively extract typical electricity price features and identify anomalies. The reasons behind these anomalies are further analyzed from both electricity consumption and trading behaviors, and corresponding improvement measures are proposed.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-7-995.shtmlpower marketelectricity pricespectral clusteringfeature extractionanomaly identification |
| spellingShingle | ZHU Feng, SHAN Chao, WU Ning, CAI Qixin, ZHU Yunan, LIU Yunpeng, ZUO Qiang Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment Shanghai Jiaotong Daxue xuebao power market electricity price spectral clustering feature extraction anomaly identification |
| title | Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment |
| title_full | Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment |
| title_fullStr | Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment |
| title_full_unstemmed | Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment |
| title_short | Feature Extraction and Anomaly Identification Method for Power Customer Price in Power Market Enviroment |
| title_sort | feature extraction and anomaly identification method for power customer price in power market enviroment |
| topic | power market electricity price spectral clustering feature extraction anomaly identification |
| url | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-7-995.shtml |
| work_keys_str_mv | AT zhufengshanchaowuningcaiqixinzhuyunanliuyunpengzuoqiang featureextractionandanomalyidentificationmethodforpowercustomerpriceinpowermarketenviroment |