UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

An empirical investigation of domestic energy data visualizations

Herrmann, MR; Brumby, DP; Cheng, L; Gilbert, XMP; Oreszczyn, T; (2021) An empirical investigation of domestic energy data visualizations. International Journal of Human Computer Studies , 152 , Article 102660. 10.1016/j.ijhcs.2021.102660. Green open access

[thumbnail of 2021_April revised manuscript.pdf]
Preview
Text
2021_April revised manuscript.pdf - Accepted Version

Download (2MB) | Preview

Abstract

Which device in your home uses the most electricity? Many people have a poor understanding of their domestic energy consumption. In this paper, we evaluated three data visualizations used to deliver feedback. These were: (1) an aggregated line graph – showing changes in total electricity consumption over time, (2) a disaggregated line graph – showing changes in electricity consumed over time but separated out at the appliance-level, and (3) an area-based visualization – showing the cumulative energy consumed by different appliances over a given time period. In an experiment, 65 participants used one of these three visualizations to make sense of the same pattern of domestic electricity data. Participants who used the area-based visualization gained a more accurate understanding of how much electricity different domestic appliances were using compared to participants who were shown time series data. These results suggest that the choice of data visualization will impact people's understanding from smart metering systems, and that appliance-wise disaggregation offers the most promising approach for visualizing domestic electricity consumption data.

Type: Article
Title: An empirical investigation of domestic energy data visualizations
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ijhcs.2021.102660
Publisher version: https://doi.org/10.1016/j.ijhcs.2021.102660
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Smart metering, Disaggregation, Information visualization, Graphical literacy, Time series data
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10128398
Downloads since deposit
39Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item