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

Yılmaz, Selin; Firth, Steven; Allinson, David

doi:10.1080/19401493.2017.1287775

Cited by 37 publications

(19 citation statements)

References 26 publications

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“…For each time step the switch-on probability would be the sum of measured "switches on" observed divided by the total number of days. Example studies using this approach are Paatero and Lund (2006), Page (2007), Gruber et al (2014) and Yilmaz et. al.…”

Section: Model Type 1: the Empirical Data Based Time-dependent Switchmentioning

confidence: 99%

“…(2017). The method of Yilmaz et al (2017) constructs a cdf (cumulative distribution function) for the number of switch-on events for every household. Then the number of switch-on events is assigned individually for every day using this cdf.…”

Section: Model Type 1: the Empirical Data Based Time-dependent Switchmentioning

confidence: 99%

“…This is done in order to treat the empirical data as TUD data with a resolution of 15 minutes and the effect of the data time resolution on the accuracy of the model can be shown. Power demand profiles of washing machines at a 5-minute resolution were calculated using the method of Yilmaz et al (2017).…”

Section: Time Resolution Of the Datamentioning

confidence: 99%

“…The hourly probability of switching on of washing machines for the 877 households estimated by Yilmaz et al (2017), Richardson et al (2010), Widén et al (2010) and Wilke et al (2009) on 10,000 weekdays are compared with empirical data is shown in Figure 5. All models agreed well with the empirical data.…”

Section: Comparison Of the Switch-on Time Profiles And The Number Of mentioning

confidence: 99%

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A cross analysis of existing methods for modelling household appliance use

Yamaguchi

Yılmaz

Prakash

et al. 2018

Journal of Building Performance Simulation

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This paper presents a cross-analysis of the existing methods for modelling the use of household appliances and aims to provide insights into modelling approaches for researchers and designers. Five factors regarding appliance use modelling that have a significant impact on the modelling performance are defined: consideration of the intra/inter-household variation, consideration of the influence of socio-demographic conditions, time resolution of the data, quantification of model calibration parameters and applicability to a variety of modelling contexts. Four existing modelling methods commonly used in literature for modelling appliance use are studied to address these factors. Monitored data of 333 multi-family buildings in Japan and a Japanese time use survey are used in the cross-analysis to simulate the switch-on time profiles for the case of washing machines. The design of future research studies (including monitoring strategies, modelling and sample sizes) are discussed to further improve the ability to model home appliance use.

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Section: Model Type 1: the Empirical Data Based Time-dependent Switchmentioning

confidence: 99%

Section: Model Type 1: the Empirical Data Based Time-dependent Switchmentioning

confidence: 99%

Section: Time Resolution Of the Datamentioning

confidence: 99%

Section: Comparison Of the Switch-on Time Profiles And The Number Of mentioning

confidence: 99%

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A cross analysis of existing methods for modelling household appliance use

Yamaguchi

Yılmaz

Prakash

et al. 2018

Journal of Building Performance Simulation

View full text Add to dashboard Cite

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“…In this scenario, an innovative modeling approach the bottom‐up stochastic occupant behavior for predicting the use of electrical appliances in domestic building by using probability and cumulative density functions . Furthermore, multiple approaches combining building surveys, indoor dry bulb temperature monitoring, and a questionnaire survey on occupant behavior was carried out in Ref by including the operation of passive and active ventilation, cooling and shading systems to better investigate the drivers and the impact of occupants’ behavior.…”

Section: Interaction Of Building Simulation With Other Thermal‐energymentioning

confidence: 99%

Uses of dynamic simulation to predict thermal‐energy performance of buildings and districts: a review

Castaldo

Pisello

2017

WIREs Energy & Environment

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The design of sustainable constructions also resilient to climate change has become a challenging issue given the increasing greenhouse emissions rate imputable to the built environment in urban areas. In this context, dynamic simulation models represent a suitable tool to support the design from very preliminary phases, since they allow an accurate prediction of the constructions requirements, their environmental performance, and indoor comfort conditions for their occupants. Therefore, starting from specific inputs, that is, weather conditions, construction technologies, materials, energy systems, operation settings, occupancy, and so forth, it is possible to estimate the realistic building energy performance. Moreover, the calibration procedures allow making the model even more representative of the field conditions of a construction. Given the massive progress carried out by the scientific community during the last decades, this paper presents a comprehensive review of the different building dynamic simulation approaches and available tools. While previous review studies focused on single separated aspects of dynamic simulations approaches, that is, calibration methods, software, simulation of single building energy systems, the aim of the present review is to propose a more holistic approach by investigating the recent scientific progress in simulating realistic dense urban environments. In this view, the review focus bridges the gap between the simulation at single-building level and simulation at the increasingly important neighborhood scale by showing the multiple benefits deriving from using dynamic simulation tools at district level, for a more reliable investigation of building performance in their urban context, where more than 50% of the global population worldwide currently lives.

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