A review of electric load classification in smart grid environment
The load data in smart grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A five-stage process model of load classification is constructed based on the summary and analysis of studies...
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| Published in | Renewable & sustainable energy reviews Vol. 24; pp. 103 - 110 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier Ltd
01.08.2013
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The load data in smart grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A five-stage process model of load classification is constructed based on the summary and analysis of studies about load classification in smart grid environment. Then, the commonly used clustering methods for load classification are summarized and briefly reviewed, and the well-known evaluation methods for load classification are also introduced. Besides, the applications of load classification, including bad data identification and correction, load forecasting and tariff setting, are discussed. Finally, an example of load classification based on Fuzzy c-means (FCM) is presented. |
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| AbstractList | The load data in smart grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A five-stage process model of load classification is constructed based on the summary and analysis of studies about load classification in smart grid environment. Then, the commonly used clustering methods for load classification are summarized and briefly reviewed, and the well-known evaluation methods for load classification are also introduced. Besides, the applications of load classification, including bad data identification and correction, load forecasting and tariff setting, are discussed. Finally, an example of load classification based on Fuzzy c-means (FCM) is presented. |
| Author | Shen, Chao Yang, Shan-lin Zhou, Kai-le |
| Author_xml | – sequence: 1 givenname: Kai-le surname: Zhou fullname: Zhou, Kai-le email: kailezhou@gmail.com, zhoukaile@mail.hfut.edu.cn organization: School of Management, Hefei University of Technology, Hefei 230009, China – sequence: 2 givenname: Shan-lin surname: Yang fullname: Yang, Shan-lin email: hgdysl@gmail.com organization: School of Management, Hefei University of Technology, Hefei 230009, China – sequence: 3 givenname: Chao surname: Shen fullname: Shen, Chao organization: School of Management, Hefei University of Technology, Hefei 230009, China |
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| Keywords | Process model Clustering methods and result evaluation methods Load classification Load classification applications Smart grid |
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| Snippet | The load data in smart grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an... |
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| SubjectTerms | Applied sciences Clustering methods and result evaluation methods Economic data Electric energy electricity Energy Energy economics Exact sciences and technology General, economic and professional studies Load classification Load classification applications Methodology. Modelling Process model Smart grid tariffs |
| Title | A review of electric load classification in smart grid environment |
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