Hollow village detection method based on non-intrusive power load monitoring
With the development of urbanization, the population and resources converge to the city, leading to different degrees of hollow village phenomenon in rural areas. Therefore, a hollow village detection model based on non-invasive load monitoring method is proposed in this paper. Firstly, the power lo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-04-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723002986 |
| _version_ | 1797821540220272640 |
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| author | Rui Liu Donglai Wang Yan Chen Rui Guo Jiaqi Shi |
| author_facet | Rui Liu Donglai Wang Yan Chen Rui Guo Jiaqi Shi |
| author_sort | Rui Liu |
| collection | DOAJ |
| description | With the development of urbanization, the population and resources converge to the city, leading to different degrees of hollow village phenomenon in rural areas. Therefore, a hollow village detection model based on non-invasive load monitoring method is proposed in this paper. Firstly, the power load data of users is collected in rural areas, and the data is processed and analyzed. A data analysis algorithm based on the improved Attention mechanism is constructed, and the existing public data sets are used for verification and improvement. Six kinds of frequently used electrical appliances among rural users were selected as typical electrical appliances, the power load curves of selected typical electrical appliances were obtained, and the long and short term memory network algorithm was used to identify the types of household electrical appliances of rural users. After algorithm recognition and analysis, the usage characteristics of typical rural electric appliances are obtained, and it is taken as the electricity consumption behavior habits of rural residents. According to their electricity consumption habits, rural residents are divided into four categories: young people living alone, the elderly, two adults and a child, and vacant houses. According to the four groups of people, the index of hollow village is determined. By judging the corresponding household population of four groups of rural residents, the selected village is judged whether it is hollow village or whether there is a tendency of hollow village. |
| first_indexed | 2024-03-13T09:54:15Z |
| format | Article |
| id | doaj.art-0a4c5a2d462b4ee095848286287afd39 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T09:54:15Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-0a4c5a2d462b4ee095848286287afd392023-05-24T04:20:42ZengElsevierEnergy Reports2352-48472023-04-019407415Hollow village detection method based on non-intrusive power load monitoringRui Liu0Donglai Wang1Yan Chen2Rui Guo3Jiaqi Shi4Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang, Liaoning 110136, ChinaKey Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang, Liaoning 110136, China; Corresponding author.State Grid Chaoyang Electric Power Supply Company, State Grid Liaoning Electric Power Supply Co., Ltd., Chaoyang, Liaoning 122000, ChinaKey Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang, Liaoning 110136, ChinaKey Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang, Liaoning 110136, ChinaWith the development of urbanization, the population and resources converge to the city, leading to different degrees of hollow village phenomenon in rural areas. Therefore, a hollow village detection model based on non-invasive load monitoring method is proposed in this paper. Firstly, the power load data of users is collected in rural areas, and the data is processed and analyzed. A data analysis algorithm based on the improved Attention mechanism is constructed, and the existing public data sets are used for verification and improvement. Six kinds of frequently used electrical appliances among rural users were selected as typical electrical appliances, the power load curves of selected typical electrical appliances were obtained, and the long and short term memory network algorithm was used to identify the types of household electrical appliances of rural users. After algorithm recognition and analysis, the usage characteristics of typical rural electric appliances are obtained, and it is taken as the electricity consumption behavior habits of rural residents. According to their electricity consumption habits, rural residents are divided into four categories: young people living alone, the elderly, two adults and a child, and vacant houses. According to the four groups of people, the index of hollow village is determined. By judging the corresponding household population of four groups of rural residents, the selected village is judged whether it is hollow village or whether there is a tendency of hollow village.http://www.sciencedirect.com/science/article/pii/S2352484723002986Hollow villageNon-intrusive power load monitoringLSTMType of electrical appliance |
| spellingShingle | Rui Liu Donglai Wang Yan Chen Rui Guo Jiaqi Shi Hollow village detection method based on non-intrusive power load monitoring Energy Reports Hollow village Non-intrusive power load monitoring LSTM Type of electrical appliance |
| title | Hollow village detection method based on non-intrusive power load monitoring |
| title_full | Hollow village detection method based on non-intrusive power load monitoring |
| title_fullStr | Hollow village detection method based on non-intrusive power load monitoring |
| title_full_unstemmed | Hollow village detection method based on non-intrusive power load monitoring |
| title_short | Hollow village detection method based on non-intrusive power load monitoring |
| title_sort | hollow village detection method based on non intrusive power load monitoring |
| topic | Hollow village Non-intrusive power load monitoring LSTM Type of electrical appliance |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723002986 |
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