A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications

Rajabi, A; Eskandari, M; Jabbari Ghadi, M; Ghavidel, S; Li, L; Zhang, J; Siano, P

A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications

Rajabi, A

Eskandari, M

Jabbari Ghadi, M

Ghavidel, S Li, L

Zhang, J

Siano, P

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Publication Type:: Journal Article
Citation:: Energy and Buildings, 2019, 203
Issue Date:: 2019-11-15

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Field	Value	Language
dc.contributor.author	Rajabi, A https://orcid.org/0000-0002-3203-9861	en_US
dc.contributor.author	Eskandari, M https://orcid.org/0000-0003-1185-9585	en_US
dc.contributor.author	Jabbari Ghadi, M https://orcid.org/0000-0002-5564-5055	en_US
dc.contributor.author	Ghavidel, S	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722	en_US
dc.contributor.author	Siano, P	en_US
dc.date.issued	2019-11-15	en_US
dc.identifier.citation	Energy and Buildings, 2019, 203	en_US
dc.identifier.issn	0378-7788	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137467
dc.description.abstract	© 2019 Elsevier B.V. The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data mining of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settlements. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems operators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various approaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use.	en_US
dc.relation.ispartof	Energy and Buildings	en_US
dc.relation.isbasedon	10.1016/j.enbuild.2019.109455	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Building & Construction	en_US
dc.title	A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications	en_US
dc.type	Journal Article
utslib.citation.volume	203	en_US
utslib.for	090607 Power and Energy Systems Engineering (excl. Renewable Power)	en_US
utslib.for	090703 Environmental Technologies	en_US
utslib.for	09 Engineering	en_US
utslib.for	12 Built Environment and Design	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-11-15T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	203	en_US

Abstract:

© 2019 Elsevier B.V. The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data mining of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settlements. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems operators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various approaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/137467