Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

© Springer International Publishing Switzerland 2015. Data mining techniques have been developed to automatically learn consumption behaviours of households from smart meter data. In this paper, recurrent routine behaviours are introduced to characterize regular consumption activities in smart meter time series. A novel algorithm is proposed to efficiently discover recurrent routine behaviours in smart meter time series by growing subsequences. We evaluate the proposed algorithm on synthetic data and demonstrate the recurrent routine behaviours extracted on a real-world dataset from the city of Kalgoorlie-Boulder in Western Australia.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
PublisherSpringer International Publishing
Pages522-533
Volume9078
ISBN (Print)9783319180328
DOIs
Publication statusPublished - 2015
EventEfficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences - Ho Chi Minh City, Vietnam
Duration: 1 Jan 2015 → …

Conference

ConferenceEfficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences
Period1/01/15 → …

Fingerprint Dive into the research topics of 'Efficient discovery of recurrent routine behaviours in smart meter time series by growing subsequences'. Together they form a unique fingerprint.

Cite this