A fast load pattern extraction approach based on dimension reduction and sampling

Zhang, T; Gu, M; Zhang, G; Lu, J

A fast load pattern extraction approach based on dimension reduction and sampling

Zhang, T Gu, M Zhang, G

Lu, J

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 1253 - 1258
Issue Date:: 2016-11-07

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Field	Value	Language
dc.contributor.author	Zhang, T	en_US
dc.contributor.author	Gu, M	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2016-11-07	en_US
dc.identifier.citation	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 1253 - 1258	en_US
dc.identifier.isbn	9781509006250	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121830
dc.description.abstract	© 2016 IEEE. This paper proposes a fast load pattern extraction approach to solve the time consuming problem in using a traditional κ-means clustering method for large volumes of load curves. The approach, based on dimension reduction and sampling, segments and averages sampling characteristic points to reduce the load curve's dimensions, then reduces the overall size of the sample data set using representative random sampling. κ-means clustering algorithm is used to extract load patterns from the representative data set, which will be used to classify the full data set. Reducing the size and dimension of the data set allows use of a less complex algorithm, and thus greatly improves the clustering speed. The validity of the approach is proven by experiments designed to evaluate the trade-off between complexity and consistency.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2016.7737832	en_US
dc.title	A fast load pattern extraction approach based on dimension reduction and sampling	en_US
dc.type	Conference Proceeding
utslib.for	080105 Expert Systems	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. This paper proposes a fast load pattern extraction approach to solve the time consuming problem in using a traditional κ-means clustering method for large volumes of load curves. The approach, based on dimension reduction and sampling, segments and averages sampling characteristic points to reduce the load curve's dimensions, then reduces the overall size of the sample data set using representative random sampling. κ-means clustering algorithm is used to extract load patterns from the representative data set, which will be used to classify the full data set. Reducing the size and dimension of the data set allows use of a less complex algorithm, and thus greatly improves the clustering speed. The validity of the approach is proven by experiments designed to evaluate the trade-off between complexity and consistency.

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http://hdl.handle.net/10453/121830