A Method for Evaluating Options for Motif Detection in Electricity Meter Data

Dent, Ian; Craig, Tony; Aickelin, Uwe; Rodden, Tom

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).

Citation

Dent, I., Craig, T., Aickelin, U., & Rodden, T. (2018). A Method for Evaluating Options for Motif Detection in Electricity Meter Data. International Journal of Data Science, 16(1), 1-28. https://doi.org/10.6339/jds.201801_16%281%29.0001

• Journal Article Type: Article
• Acceptance Date: Dec 5, 2017
• Online Publication Date: Feb 24, 2021
• Publication Date: 2018-01
• Deposit Date: Dec 11, 2017
• Publicly Available Date: Jan 31, 2018
• Journal: Journal of Data Science
• Print ISSN: 2053-0811
• Electronic ISSN: 1683-8602
• Publisher: Inderscience
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 1
• Pages: 1-28
• DOI: https://doi.org/10.6339/jds.201801_16%281%29.0001
• Keywords: Motif detection, Clustering, Electricity Usage
• Public URL: https://nottingham-repository.worktribe.com/output/907665
• Publisher URL: http://www.jds-online.com/volume-16-number-1-january-2018