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

Leticia Arco, Gladys Casas, Ann Nowe

Onderzoeksoutput: Conference paperResearch

2 Citaten (Scopus)

Samenvatting

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.
Originele taal-2English
TitelIML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning
UitgeverijAssociation for Computing Machinery (ACM)
Aantal pagina's13
ISBN van elektronische versie978-1-4503-5243-7
DOI's
StatusPublished - 17 okt 2017
Evenement1st International Conference on Internet of Things and Machine Learning - Liverpool, United Kingdom
Duur: 17 okt 201718 okt 2017

Conference

Conference1st International Conference on Internet of Things and Machine Learning
Verkorte titelIML
Land/RegioUnited Kingdom
StadLiverpool
Periode17/10/1718/10/17

Vingerafdruk

Duik in de onderzoeksthema's van 'Clustering methodology for smart metering data based on local and global features'. Samen vormen ze een unieke vingerafdruk.

Citeer dit