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

Selin Yilmaz,Steven K Firth,David Allinson

doi:10.1080/19401493.2017.1287775

Selin Yilmaz, Steven K Firth + Show 1 more

Open Access

https://doi.org/10.1080/19401493.2017.1287775

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Abstract

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.

Highlights

Occupant behaviour in buildings can be defined as both the occupant presence and the occupant actions that may influence the building environmental conditions and the building energy consumption (Yan and Hong 2014)
The results show that one chest freezer has an average of 109.7 switch-on events per day throughout its monitoring period and another had an average of 5.5
One TV 1 has an average of 5.8 switch-on events per day throughout its monitoring period and another had an average of 0.04

Summary

Introduction

Occupant behaviour in buildings can be defined as both the occupant presence and the occupant actions that may influence the building environmental conditions and the building energy consumption (Yan and Hong 2014) These include the occupants’ operation of windows, air conditioning systems and heating systems (such as window opening, timer settings and choice of thermostat set-points) that affect hygrothermal conditions, indoor air quality, light, noise and temperature (Guerra Santin, Itard, and Visscher 2009; Hoes et al 2009; Schweiker et al 2012). Appliances such as water heating and cooking appliances were monitored directly from the consumer unit of the house using the MultivoiesTM system which was installed inside the consumer unit (DECC 2014a)

Results

Discussion

Conclusion