Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction
Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Con...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1326257/full |
| _version_ | 1797367655047364608 |
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| author | Bing Kang Bing Kang Bing Kang Zhihao Xu Zhihao Xu Zhihao Xu Wenhua He Guili Ding Wei Han Min Sun Junjia He Zongyao Wang |
| author_facet | Bing Kang Bing Kang Bing Kang Zhihao Xu Zhihao Xu Zhihao Xu Wenhua He Guili Ding Wei Han Min Sun Junjia He Zongyao Wang |
| author_sort | Bing Kang |
| collection | DOAJ |
| description | Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation. |
| first_indexed | 2024-03-08T17:21:23Z |
| format | Article |
| id | doaj.art-061a14ef346b42c1bea1baca36fbc49c |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-08T17:21:23Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-061a14ef346b42c1bea1baca36fbc49c2024-01-03T04:39:56ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-01-011110.3389/fenrg.2023.13262571326257Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reductionBing Kang0Bing Kang1Bing Kang2Zhihao Xu3Zhihao Xu4Zhihao Xu5Wenhua He6Guili Ding7Wei Han8Min Sun9Junjia He10Zongyao Wang11Nanchang Institute of Technology, Nanchang, ChinaSchool of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Grid Jiangxi Electric Power Research Institute, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaSchool of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaJiangxi Booway New Technology Co., Ltd., Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaNanchang Institute of Technology, Nanchang, ChinaState Grid Jiangxi Electric Power Research Institute, Nanchang, ChinaSchool of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaNanchang Institute of Technology, Nanchang, ChinaAccurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1326257/fullpower systemintegrated clusteringenergy consumption characteristicsdouble-layer clusteringenergy saving and carbon reduction |
| spellingShingle | Bing Kang Bing Kang Bing Kang Zhihao Xu Zhihao Xu Zhihao Xu Wenhua He Guili Ding Wei Han Min Sun Junjia He Zongyao Wang Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction Frontiers in Energy Research power system integrated clustering energy consumption characteristics double-layer clustering energy saving and carbon reduction |
| title | Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction |
| title_full | Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction |
| title_fullStr | Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction |
| title_full_unstemmed | Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction |
| title_short | Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction |
| title_sort | research on the double layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction |
| topic | power system integrated clustering energy consumption characteristics double-layer clustering energy saving and carbon reduction |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1326257/full |
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