The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies
In recent years, as global climate change has intensified, carbon emissions management in the industrial sector has become a critical area in addressing climate change. The application of intelligent algorithms in carbon emissions prediction and management offers new possibilities for implementing e...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/15/e3sconf_eppc2025_01012.pdf |
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| author | Xu Shasha Wu Haipeng Luo Junting Chen Jian Jia Huihan |
| author_facet | Xu Shasha Wu Haipeng Luo Junting Chen Jian Jia Huihan |
| author_sort | Xu Shasha |
| collection | DOAJ |
| description | In recent years, as global climate change has intensified, carbon emissions management in the industrial sector has become a critical area in addressing climate change. The application of intelligent algorithms in carbon emissions prediction and management offers new possibilities for implementing effective emission control strategies. This paper, based on the Support Vector Machine (SVM) model, explores its application in industrial carbon accounting, focusing on the interaction between carbon emissions prediction and optimization of control strategies. By analyzing the differences between predicted results and actual carbon emissions data, the paper proposes a series of emission control strategies driven by intelligent algorithms, and discusses them in the context of policy environments and production characteristics. The study shows that the SVM model demonstrates high accuracy in carbon emissions prediction, effectively supporting corporate carbon management decisions. |
| format | Article |
| id | doaj-art-5df0770c2efa491fa6dbfb4fd382eb4a |
| institution | DOAJ |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-5df0770c2efa491fa6dbfb4fd382eb4a2025-08-20T03:02:18ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016150101210.1051/e3sconf/202561501012e3sconf_eppc2025_01012The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control StrategiesXu Shasha0Wu Haipeng1Luo Junting2Chen Jian3Jia Huihan4State Grid Information & Telecommunication Group Tianjin Richsoft Electric Power Information Technology Co., Ltd.State Grid Information & Telecommunication Group Tianjin Richsoft Electric Power Information Technology Co., Ltd.State Grid Information & Telecommunication Group Tianjin Richsoft Electric Power Information Technology Co., Ltd.State Grid Information & Telecommunication Group Tianjin Richsoft Electric Power Information Technology Co., Ltd.State Grid Information & Telecommunication Group Tianjin Richsoft Electric Power Information Technology Co., Ltd.In recent years, as global climate change has intensified, carbon emissions management in the industrial sector has become a critical area in addressing climate change. The application of intelligent algorithms in carbon emissions prediction and management offers new possibilities for implementing effective emission control strategies. This paper, based on the Support Vector Machine (SVM) model, explores its application in industrial carbon accounting, focusing on the interaction between carbon emissions prediction and optimization of control strategies. By analyzing the differences between predicted results and actual carbon emissions data, the paper proposes a series of emission control strategies driven by intelligent algorithms, and discusses them in the context of policy environments and production characteristics. The study shows that the SVM model demonstrates high accuracy in carbon emissions prediction, effectively supporting corporate carbon management decisions.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/15/e3sconf_eppc2025_01012.pdf |
| spellingShingle | Xu Shasha Wu Haipeng Luo Junting Chen Jian Jia Huihan The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies E3S Web of Conferences |
| title | The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies |
| title_full | The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies |
| title_fullStr | The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies |
| title_full_unstemmed | The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies |
| title_short | The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies |
| title_sort | application of support vector machine svm in industrial carbon accounting prediction and green electricity control strategies |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/15/e3sconf_eppc2025_01012.pdf |
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