A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context
Abstract To achieve the rapid unsupervised learning of multi-source information, this paper studies a multi-source information integration method for the “dual carbon” smart monitoring center based on the improved federated learning. To solve the problem of rapid integration information from many so...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-06-01
|
| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-025-00537-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850136987512602624 |
|---|---|
| author | Jia Liu Zhenhua Yan Liang Wang Wenni Kang Jiangbo Sha |
| author_facet | Jia Liu Zhenhua Yan Liang Wang Wenni Kang Jiangbo Sha |
| author_sort | Jia Liu |
| collection | DOAJ |
| description | Abstract To achieve the rapid unsupervised learning of multi-source information, this paper studies a multi-source information integration method for the “dual carbon” smart monitoring center based on the improved federated learning. To solve the problem of rapid integration information from many sources in the “dual carbon” smart monitoring center, a multimodal federated learning framework is built on the basis of the traditional federated learning. The generator and discriminator of the conditional generative adversarial network model are used to distinguish between the generated pseudo-samples and normal samples, and the multi-source information is obtained unsupervisedly. Based on the global distribution, the fast integration is achieved by using the passive distillation method of federated data. At the same time, the stochastic gradient descent is used to enhance the learning rate, improve the learning ability of the model, and promote the unsupervised fast fusion. The experiment shows that this method can effectively integrate the multi-source information, display the spatial status of carbon emissions and enterprise energy production data. The integrated information has high completeness and entropy value, and is accurate and applicable in the multi-source information integration of the “dual carbon” smart monitoring center. |
| format | Article |
| id | doaj-art-5df13c9dcf9c436bb4a62c57d8d9e0ba |
| institution | OA Journals |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-5df13c9dcf9c436bb4a62c57d8d9e0ba2025-08-20T02:30:59ZengSpringerOpenEnergy Informatics2520-89422025-06-018111910.1186/s42162-025-00537-1A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon contextJia Liu0Zhenhua Yan1Liang Wang2Wenni Kang3Jiangbo Sha4State Grid Ningxia Electric Power Co., Ltd, Technical Research InstituteState Grid Ningxia Electric Power Co., Ltd, Technical Research InstituteState Grid Ningxia Electric Power Co., LtdState Grid Ningxia Electric Power Co., Ltd, Technical Research InstituteState Grid Ningxia Electric Power Co., Ltd, Technical Research InstituteAbstract To achieve the rapid unsupervised learning of multi-source information, this paper studies a multi-source information integration method for the “dual carbon” smart monitoring center based on the improved federated learning. To solve the problem of rapid integration information from many sources in the “dual carbon” smart monitoring center, a multimodal federated learning framework is built on the basis of the traditional federated learning. The generator and discriminator of the conditional generative adversarial network model are used to distinguish between the generated pseudo-samples and normal samples, and the multi-source information is obtained unsupervisedly. Based on the global distribution, the fast integration is achieved by using the passive distillation method of federated data. At the same time, the stochastic gradient descent is used to enhance the learning rate, improve the learning ability of the model, and promote the unsupervised fast fusion. The experiment shows that this method can effectively integrate the multi-source information, display the spatial status of carbon emissions and enterprise energy production data. The integrated information has high completeness and entropy value, and is accurate and applicable in the multi-source information integration of the “dual carbon” smart monitoring center.https://doi.org/10.1186/s42162-025-00537-1Dual carbonImproved federated learningSmart monitoring centerMultiple-source information |
| spellingShingle | Jia Liu Zhenhua Yan Liang Wang Wenni Kang Jiangbo Sha A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context Energy Informatics Dual carbon Improved federated learning Smart monitoring center Multiple-source information |
| title | A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context |
| title_full | A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context |
| title_fullStr | A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context |
| title_full_unstemmed | A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context |
| title_short | A rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context |
| title_sort | rapid multi source information integration method based on improved federated learning for a dual carbon smart monitoring center in a dual carbon context |
| topic | Dual carbon Improved federated learning Smart monitoring center Multiple-source information |
| url | https://doi.org/10.1186/s42162-025-00537-1 |
| work_keys_str_mv | AT jialiu arapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT zhenhuayan arapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT liangwang arapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT wennikang arapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT jiangbosha arapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT jialiu rapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT zhenhuayan rapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT liangwang rapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT wennikang rapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext AT jiangbosha rapidmultisourceinformationintegrationmethodbasedonimprovedfederatedlearningforadualcarbonsmartmonitoringcenterinadualcarboncontext |