Rezvany, Negar;
Hillel, Tim;
Bierlaire, Michel;
(2023)
From domestic energy demand to household activity patterns.
In:
Proceedings of the 23rd Swiss Transport Research Conference (STRC 2023).
Swiss Transport Research Conference: Ascona, Switzerland.
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Abstract
Residential building energy usage can be considered as being derived from the activity patterns of individuals inside the home. As such an activity-based energy demand model that can create in-home energy usage profiles from household activity patterns is the key to a better building energy demand analysis. In order to find the relation between building energy usage and activity profiles, energy usage data with an overlapping activity diary survey is needed. However, there is no detailed data containing information on both household activity schedules and energy usage. Therefore, utilizing a Bayesian approach, we explore the possibility of reverse engineering to get the household activity patterns from energy usage profiles. The findings can be further used for linking the domestic energy demand to the activity schedules of the occupants.
Type: | Proceedings paper |
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Title: | From domestic energy demand to household activity patterns |
Event: | 23rd Swiss Transport Research Conference (STRC 2023) |
Location: | Ascona, Switzerland |
Dates: | 10 May 2023 - 12 May 2023 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://www.strc.ch/2023/Rezvany_EtAl.pdf |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Activity-based energy demand analysis, Activity-based modelling, Building energy usage data |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174110 |
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