A power extraction approach with load state modification for energy disaggregation
Energy disaggregation is a technology that disassembles the energy consumption from the entire house into load-level contributions. One of the foundational tasks for this technology is to accurately ascertain the truth electrical energy consumption of the target load. However, current energy disaggr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001277 |
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| author | Yusen Zhang Feng Gao Kangjia Zhou Shuquan Wang Hanzhi Wang |
| author_facet | Yusen Zhang Feng Gao Kangjia Zhou Shuquan Wang Hanzhi Wang |
| author_sort | Yusen Zhang |
| collection | DOAJ |
| description | Energy disaggregation is a technology that disassembles the energy consumption from the entire house into load-level contributions. One of the foundational tasks for this technology is to accurately ascertain the truth electrical energy consumption of the target load. However, current energy disaggregation methods find it difficult to accurately predict the actual operating power of appliances when there are significant differences in the data distribution of appliances across various scenarios due to the diversity in manufacturers, usage times, and operating conditions. In this study, we propose a power extraction approach with load state modification to capture accurate load operating power with minimal influence from usage scenarios. To be specific, the on/off state sequence of appliances is first predicted leveraging existing energy disaggregation methods, and two state modification methods based on non-operating time and operating time of appliances are respectively proposed to modify the erroneous states in sequence. Subsequently, the power extraction approach calculates the operational power of target appliance based on the amplitude of fluctuations within the aggregated energy consumption caused by its state changes. Furthermore, a removing signal spikes method is proposed to improve the accuracy of the extracted power value. We conducted extensive experiments on a public dataset, demonstrating that the proposed method can significantly improve the accuracy of state-of-the-art solution. The average of mean absolute error across commonly used appliances during on state were reduced by 44.75 % and 32.07 % respectively in the UK-DALE and REFIT datasets. |
| format | Article |
| id | doaj-art-d46a80d2dbb14979abfdb969fd0df652 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-d46a80d2dbb14979abfdb969fd0df6522025-01-27T04:22:20ZengElsevierEnergy and AI2666-54682025-01-0119100461A power extraction approach with load state modification for energy disaggregationYusen Zhang0Feng Gao1Kangjia Zhou2Shuquan Wang3Hanzhi Wang4School of Control Science and Engineering, Shandong University, Jinan 250012, PR ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250012, PR China; Corresponding author.School of Electrical Engineering, Shandong University, Jinan 250012, PR ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250012, PR ChinaShandong University Electric Power Technology Co. Ltd, Jinan 250012, PR ChinaEnergy disaggregation is a technology that disassembles the energy consumption from the entire house into load-level contributions. One of the foundational tasks for this technology is to accurately ascertain the truth electrical energy consumption of the target load. However, current energy disaggregation methods find it difficult to accurately predict the actual operating power of appliances when there are significant differences in the data distribution of appliances across various scenarios due to the diversity in manufacturers, usage times, and operating conditions. In this study, we propose a power extraction approach with load state modification to capture accurate load operating power with minimal influence from usage scenarios. To be specific, the on/off state sequence of appliances is first predicted leveraging existing energy disaggregation methods, and two state modification methods based on non-operating time and operating time of appliances are respectively proposed to modify the erroneous states in sequence. Subsequently, the power extraction approach calculates the operational power of target appliance based on the amplitude of fluctuations within the aggregated energy consumption caused by its state changes. Furthermore, a removing signal spikes method is proposed to improve the accuracy of the extracted power value. We conducted extensive experiments on a public dataset, demonstrating that the proposed method can significantly improve the accuracy of state-of-the-art solution. The average of mean absolute error across commonly used appliances during on state were reduced by 44.75 % and 32.07 % respectively in the UK-DALE and REFIT datasets.http://www.sciencedirect.com/science/article/pii/S2666546824001277Non-intrusive load monitoringEnergy disaggregationState modificationEvent detection |
| spellingShingle | Yusen Zhang Feng Gao Kangjia Zhou Shuquan Wang Hanzhi Wang A power extraction approach with load state modification for energy disaggregation Energy and AI Non-intrusive load monitoring Energy disaggregation State modification Event detection |
| title | A power extraction approach with load state modification for energy disaggregation |
| title_full | A power extraction approach with load state modification for energy disaggregation |
| title_fullStr | A power extraction approach with load state modification for energy disaggregation |
| title_full_unstemmed | A power extraction approach with load state modification for energy disaggregation |
| title_short | A power extraction approach with load state modification for energy disaggregation |
| title_sort | power extraction approach with load state modification for energy disaggregation |
| topic | Non-intrusive load monitoring Energy disaggregation State modification Event detection |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001277 |
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