| Huang Yunyou (黄运有)* **,Wang Nana** ***,Hao Tianshu** ***,Guo Xiaoxu**,Luo Chunjie** ***,Wang Lei**,Ren Rui**,Zhan Jianfeng** ***.[J].高技术通讯(英文),2021,27(1):53~61 |
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| Clustering residential electricity load curve via community detection in network |
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| DOI：10.3772/j.issn.1006-6748.2021.01.007 |
| 中文关键词: |
| 英文关键词: smart meter data, electricity load curve (LC), clustering methods, community detection, demand response (DR) |
| 基金项目: |
| Author Name | Affiliation | | Huang Yunyou (黄运有)* ** | (*School of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, P.R.China)
(**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | Wang Nana** *** | (**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China)
(***University of Chinese Academy of Sciences, Beijing 100049, P.R.China) | | Hao Tianshu** *** | (**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China)
(***University of Chinese Academy of Sciences, Beijing 100049, P.R.China) | | Guo Xiaoxu** | (**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | Luo Chunjie** *** | (**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China)
(***University of Chinese Academy of Sciences, Beijing 100049, P.R.China) | | Wang Lei** | (**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | Ren Rui** | (**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | Zhan Jianfeng** *** | (**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China)
(***University of Chinese Academy of Sciences, Beijing 100049, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Performing analytics on the load curve (LC) of customers is the foundation for demand response which requires a better understanding of customers’ consumption pattern (CP) by analyzing the load curve. However, the performances of previous widely-used LC clustering methods are poor in two folds: larger number of clusters, huge variances within a cluster (a CP is extracted from a cluster), bringing huge difficulty to understand the electricity consumption pattern of customers. In this paper, to improve the performance of LC clustering, a clustering framework incorporated with community detection is proposed. The framework includes three parts: network construction, community detection, and CP extraction. According to the cluster validity index (CVI), the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters. And the approach needs fewer clusters to achieve the same performance measured by CVI. |
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