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Occupant behaviour modelling in domestic buildings: the case of household electrical appliances

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journal contribution
posted on 2017-02-16, 11:13 authored by Selin Yilmaz, Steven FirthSteven Firth, David AllinsonDavid Allinson
This paper presents a new approach to bottom-up stochastic occupant behaviour modelling for predicting the use of household electrical appliances in domestic buildings. Three metrics relating to appliance occupant behaviours are defined: the number of switch-on events per day, the switch-on times, and the duration of each appliance usage. The metrics were calculated for 1,076 appliances in 225 households from the UK Government’s Household Electricity Survey carried out in 2010-2011. The analysis shows that occupant behaviour varies substantially between households, across appliance types and over time. The new modelling approach improves on previous approaches by using a three step process where the three appliance occupant behaviour metrics are simulated respectively using stochastic processes to capture daily variations in appliance occupant behaviour. It uses probability and cumulative density functions based on individual households and appliances which are shown to have advantages for modelling the variations in appliance occupant behaviours.

Funding

This research was made possible by Engineering and Physical Sciences Research Council (EPSRC) support for the London-Loughborough Centre for Doctoral Research in Energy Demand (Grant EP/H009612/1).

History

School

  • Architecture, Building and Civil Engineering

Published in

Journal of Building Performance Simulation

Volume

10

Issue

5-6

Pages

582-600

Citation

YILMAZ, S., FIRTH, S.K. and ALLINSON, D., 2017. Occupant behaviour modelling in domestic buildings: the case of household electrical appliances. Journal of Building Performance Simulation, 10(5-6), pp.582-600.

Publisher

Informa UK Limited, trading as Taylor & Francis Group (© the authors)

Version

  • VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2017-01-24

Publication date

2017-02-17

Copyright date

2017

Notes

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ISSN

1940-1493

eISSN

1940-1507

Language

  • en