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

Leticia Arco, Gladys Casas, Ann Nowe

Research output: Chapter in Book/Report/Conference proceedingConference paper

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationIML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning
PublisherAssociation for Computing Machinery (ACM)
Number of pages13
ISBN (Electronic)978-1-4503-5243-7
DOIs
Publication statusPublished - 17 Oct 2017
Event1st International Conference on Internet of Things and Machine Learning - Liverpool, United Kingdom
Duration: 17 Oct 201718 Oct 2017

Conference

Conference1st International Conference on Internet of Things and Machine Learning
Abbreviated titleIML
CountryUnited Kingdom
CityLiverpool
Period17/10/1718/10/17

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