research-article

Clustering methodology for smart metering data based on local and global features

Authors:

Gladys Casas, and

Ann NowéAuthors Info & Claims

IML '17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning

October 2017

Article No.: 65, Pages 1 - 13

https://doi.org/10.1145/3109761.3158398

Published: 17 October 2017 Publication History

Abstract

In order to develop real intelligent smart grids, understanding the patterns hidden in the smart grid data is crucial. More precisely, the detection of the preferences, behavior and characteristics of consumers and prosumers is crucial. In this work, we introduce a general methodology that groups energy consumption and production of households, based on global as well as local features, allowing the characterization of load and production profiles for consumers and prosumers. Our methodology is illustrated using a two-level clustering approach. The theoretical results are applicable in other areas, and have utility in a general business analysis.

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Cited By

Monawwar HAbedrabboh KAlmarri OAl-Fagih L(2022)Investigating Demand-Side Management (DSM) Opportunities Using Load Profiling: The Case of Qatar2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)10.1109/ISGTAsia54193.2022.10003591(399-403)Online publication date: 1-Nov-2022
https://doi.org/10.1109/ISGTAsia54193.2022.10003591
Efkarpidis NGeidl MWache HPeter MAdam MEfkarpidis NGeidl MWache HPeter MAdam M(2022)Smart Metering ApplicationsSmart Metering Applications10.1007/978-3-031-05737-3_3(13-124)Online publication date: 4-Oct-2022
https://doi.org/10.1007/978-3-031-05737-3_3
Piscitelli MBrandi SCapozzoli A(2019)Recognition and classification of typical load profiles in buildings with non-intrusive learning approachApplied Energy10.1016/j.apenergy.2019.113727255(113727)Online publication date: Dec-2019
https://doi.org/10.1016/j.apenergy.2019.113727

Index Terms

Clustering methodology for smart metering data based on local and global features
1. Applied computing
  1. Enterprise computing
    1. Enterprise information systems
      1. Enterprise applications
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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cover image ACM Other conferences

IML '17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning

October 2017

581 pages

ISBN:9781450352437

DOI:10.1145/3109761

General Chairs:
Hani Hamdan
University of Paris-Saclay, Paris, France
,
Djallel Eddine Boubiche
University of Batna, Algeria
,
Program Chair:
Fanny Klett
German Workforce ADL Partnership Laboratory, Germany

Copyright © 2017 ACM.

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Publication History

Published: 17 October 2017

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IML 2017: International Conference on Internet of Things and Machine Learning

October 17 - 18, 2017

Liverpool, United Kingdom

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Cited By

Monawwar HAbedrabboh KAlmarri OAl-Fagih L(2022)Investigating Demand-Side Management (DSM) Opportunities Using Load Profiling: The Case of Qatar2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)10.1109/ISGTAsia54193.2022.10003591(399-403)Online publication date: 1-Nov-2022
https://doi.org/10.1109/ISGTAsia54193.2022.10003591
Efkarpidis NGeidl MWache HPeter MAdam MEfkarpidis NGeidl MWache HPeter MAdam M(2022)Smart Metering ApplicationsSmart Metering Applications10.1007/978-3-031-05737-3_3(13-124)Online publication date: 4-Oct-2022
https://doi.org/10.1007/978-3-031-05737-3_3
Piscitelli MBrandi SCapozzoli A(2019)Recognition and classification of typical load profiles in buildings with non-intrusive learning approachApplied Energy10.1016/j.apenergy.2019.113727255(113727)Online publication date: Dec-2019
https://doi.org/10.1016/j.apenergy.2019.113727

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