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The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentive-based, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.


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Load Profiling and Its Application to Demand Response: A Review

Show Author's information Yi WangQixin ChenChongqing Kang( )Mingming ZhangKe WangYun Zhao
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China.
Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510080, China

Abstract

The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentive-based, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.

Keywords:

load profiling, demand response, data mining, customer segmentation, Advanced Metering Infrastructure (AMI)
Received: 09 February 2015 Accepted: 09 March 2015 Published: 23 April 2015 Issue date: April 2015
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Publication history

Received: 09 February 2015
Accepted: 09 March 2015
Published: 23 April 2015
Issue date: April 2015

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© The author(s) 2015

Acknowledgements

This work was supported by the National Science Fund for Distinguished Young Scholars (No. 51325702).

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