A Habit Detection Algorithm (HDA) for Discovering Recurrent Patterns in Smart Meter Time Series

Research output: Chapter in Book/Conference paperChapter

4 Citations (Scopus)
10 Downloads (Pure)

Abstract

Conserving water is a critical problem and characterising how households in communities use water is a first step for reducing consumption. This paper introduces a method for discovering habits in smart water meter time series. Habits are household activities that recur in a predictable way, such as watering the garden at 6 am twice a week. Discovering habit patterns automatically is a challenging data mining task. Habit patterns are not only periodic, nor only seasonal, and they may not be frequent. Their recurrences are partial periodic patterns with a very large number of candidates. Further, the recurrences in real data are imperfect, making accurate matching of observations with proposed patterns difficult. The main contribution of this paper is an efficient, robust and accurate Habit Detection Algorithm (HDA) for discovering regular activities in smart meter time series with evaluation the performance of the algorithm and its ability to discover valuable insights from real-world data sets.
Original languageEnglish
Title of host publicationBig Data Analytics in the Social and Ubiquitous Context
EditorsMartin Atzmueller, Alvin Chin, Frederik Janssen, Immanuel Schweizer, Christoph Trattner
PublisherSpringer
Pages109-127
Number of pages19
Volume9546
ISBN (Electronic)ISBN 978-3-319-29009-6
ISBN (Print)9783319290089
DOIs
Publication statusPublished - 2016
Event5th International Workshop on Mining Ubiquitous and Social Environments - Nancy, France
Duration: 15 Sep 201415 Sep 2014

Publication series

NameLecture Notes in Computer Science
Volume9546

Conference

Conference5th International Workshop on Mining Ubiquitous and Social Environments
CountryFrance
CityNancy
Period15/09/1415/09/14

Fingerprint Dive into the research topics of 'A Habit Detection Algorithm (HDA) for Discovering Recurrent Patterns in Smart Meter Time Series'. Together they form a unique fingerprint.

Cite this