Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE
The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361916/full |
| _version_ | 1827283536677175296 |
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| author | Zengxin Pu Yu Huang Min Weng Yang Meng Yunbin Zhao Gengsheng He |
| author_facet | Zengxin Pu Yu Huang Min Weng Yang Meng Yunbin Zhao Gengsheng He |
| author_sort | Zengxin Pu |
| collection | DOAJ |
| description | The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM. |
| first_indexed | 2024-04-24T09:37:08Z |
| format | Article |
| id | doaj.art-0dd4d70a925e40ff82d189c125ef1543 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-24T09:37:08Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-0dd4d70a925e40ff82d189c125ef15432024-04-15T07:47:14ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-04-011210.3389/fenrg.2024.13619161361916Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAEZengxin Pu0Yu Huang1Min Weng2Yang Meng3Yunbin Zhao4Gengsheng He5Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang, ChinaElectric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang, ChinaGuiyang Power Supply Bureau of Guizhou Power Grid Co., Ltd., Guiyang, ChinaDuyun Power Supply Bureau of Guizhou Power Grid Co., Ltd., Duyun, ChinaGuiyang Power Supply Bureau of Guizhou Power Grid Co., Ltd., Guiyang, ChinaEnergy Development Research Institute, China Southern Power Grid, Guangzhou, ChinaThe construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361916/fulldeep learningnon-intrusive load monitoringdenoising autoencoderweather featurecalendar feature |
| spellingShingle | Zengxin Pu Yu Huang Min Weng Yang Meng Yunbin Zhao Gengsheng He Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE Frontiers in Energy Research deep learning non-intrusive load monitoring denoising autoencoder weather feature calendar feature |
| title | Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE |
| title_full | Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE |
| title_fullStr | Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE |
| title_full_unstemmed | Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE |
| title_short | Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE |
| title_sort | enhancing non intrusive load monitoring with weather and calendar feature integration in dae |
| topic | deep learning non-intrusive load monitoring denoising autoencoder weather feature calendar feature |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361916/full |
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