A method for evaluating options for motif detection in electricity meter data

Ian Dent, Tony Craig, Uwe Aickelin, Tom Rodden

Research output: Journal PublicationArticlepeer-review

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Abstract

Investigation of household electricity usage patterns, and matching the patterns to behaviours, is an important area of research given the centrality of such patterns in addressing the needs of the electricity industry. Additional knowledge of household behaviours will allow more effective targeting of demand side management (DSM) techniques. This paper addresses the question as to whether a reasonable number of meaningful motifs, that each represent a regular activity within a domestic household, can be identified solely using the household level electricity meter data. Using UK data collected from several hundred households in Spring 2011 monitored at a frequency of five minutes, a process for finding repeating short patterns (motifs) is defined. Different ways of representing the motifs exist and a qualitative approach is presented that allows for choosing between the options based on the number of regular behaviours detected (neither too few nor too many).
Original languageEnglish
JournalJournal of Data Science
Volume16
Issue number1
Early online date31 Jan 2018
Publication statusPublished Online - 31 Jan 2018

Keywords

  • Motif detection, Clustering, Electricity Usage

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