A bottom-up stochastic model to predict building occupants' time-dependent activities

Wilke, Urs; Haldi, Frédéric; Scartezzini, Jean-Louis; Robinson, Darren

doi:10.1016/j.buildenv.2012.10.021

Cited by 168 publications

(88 citation statements)

References 25 publications

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“…A review of the literature identified a number of occupant behaviours for household appliance usage including switch-on times, usage durations, choice of power mode for the appliance operation and behaviour towards stand-by (Capasso et al 1994;Paatero and Lund 2006;Page 2007;Richardson et al 2010;Widén et al 2011;Yamaguchi, Fujimoto, and Shimoda 2011;Wilké et al 2013). Table 2 summarizes the appliance behaviour metrics chosen for this study based on the literature review and includes the metric definitions, to which appliance type they are applied and the influencing factors of occupant behaviour and appliance characteristics.…”

Section: Choice Of Appliance Behaviour Metricsmentioning

confidence: 99%

“…Typically, these models are based on one-day diaries, from Time of Use (TOU) surveys, reporting households' daily activities of 5000-10,000 households (Tanimoto, Hagishima, and Sagara 2008;Richardson et al 2010;Widén, Molin, and Ellegård 2012;Wilké et al 2013). There are also long-term observational studies where appliances were monitored for extended periods using electrical power sensors (Page 2007).…”

Section: Introductionmentioning

confidence: 99%

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

Yılmaz

Firth

Allinson

2017

Journal of Building Performance Simulation

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Section: Choice Of Appliance Behaviour Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yılmaz

Firth

Allinson

2017

Journal of Building Performance Simulation

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“…For example, some purely activity-based models do not simulate actual household energy demand (Aerts, Minnen, Glorieux, Wouters, & Descamps, 2014;López-Rodríguez, Santiago, Trillo-Montero, Torriti, & Moreno-Munoz, 2013;Wilke, Haldi, Scartezzini, & Robinson, 2013), while Paatero and Lund (2006) simulate appliance use based on deriving switch-on probabilities from monitored electricity consumption data, rather than activity information.…”

Section: The Need For Improved Tools To Evaluate Demand Responsementioning

confidence: 99%

“…Recognising the limitations above, there is a move towards more complex models of activity (Aerts et al, 2014;Flett & Kelly, 2016;Wilke et al, 2013). These are characterised by i) the use of higher-order Markovchains, or 'survival models' of activities, and ii) by providing greater detail about the characteristics of the household occupants and their occupancy patterns.…”

Section: Interconnected Activities and Appliancesmentioning

confidence: 99%

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

et al. 2017

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Demand response is receiving increasing interest as a new form of flexibility within low-carbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning 'bottom-up' activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between appliance usage and activities are not evidence-based. We propose new data collection approaches to address this gap. Second, aside from thermal comfort, service expectations, which can be an important source of flexibility, are underrepresented and their inclusion into demand models would improve their predicative power in this area. Finally, flexibility can be present over a range of time scales, from immediate responses, to longer term trends. Longitudinal time use data from participants in demand response schemes may be able to illuminate these. The recommendations of this paper seek to enhance the current state-of-the-art in activity-based models and to provide useful tools for the assessment of demand response.

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“…The former is introduced in early work of by C.Walker () and A.Capasso () et al (Walker, 1982;Walker & Pokoski, 1985;Capasso et al , 1993Capasso et al , , 1994, and is adopted by a majority of load models (Jordan & Vajen, 2001b,a;Spur et al , 2006;Widén et al , 2009a,b;Widén et al , 2012;Stokes et al , 2004;Richardson et al , 2008Richardson et al , , 2009Richardson et al , , 2010Richardson, 2010;Wilke et al , 2013;? ;Aerts, 2015).…”

Section: Prior Considerationsmentioning

confidence: 99%

Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour

Bætens

Sælens

2015

Journal of Building Performance Simulation

109

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Occupant behaviour has since long been of main interest in the domain of building energy savings and indoor air quality; and its importance is recognized by its wide coverage in literature. In the recent developments of detailed transient building energy simulations, including the occupant behaviour as boundary condition for the thermal comfort and system efficiency calculations has been a major research topic given its significant impact. Simultaneous growing interest in district energy simulations raises similar questions at the aggregate level, where upscaling from the building to an aggregate neighborhood level at the spatial scale of a low-voltage feeder results in a natural regression to the mean lowering uncertainty compared to the level of the household.The presented work starts with the description of StROBe, a stochastic residential occupant behaviour for district energy simulations; integrating the modelling of receptacle loads, internal heat gains, thermostat settings and hot water tappings based on occupancy and activity prerequisites. Given this model, the uncertainty for district energy simulations is addressed. The epistemic uncertainties are aleborated first comparing model results with reference values and denoting local disaggregation of demographic statistics as possible main hiatus of general modelling methods for building energy occupant behaviour used at the neighborhood level. To conclude, the aleatory uncertainty caused by stochastic residential occupant behaviour in integrated district energy simulations are quantified. Here, the expected value of the objective functions have to a large extend the same minimizers as the measures of the proposed robustness. As such, optimizing an objective value for its expected value generally seems to result in a optimum near the optimum of robustness. However, 95 percent of the observed objectives lay between 0.81 and 1.6 times the expected value for a feeder larger than 10 houses and between 0.88 and 1.3 times the expected value for more than 20 houses denoting an overall 'rather small' uncertainty on the possible objective functions caused by user behaviour. Furthermore, we show that the design of the building energy system has its impact on the robustness of the objective criteria and it could thus be minimized as part of an optimiation exercise.

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A bottom-up stochastic model to predict building occupants' time-dependent activities

Cited by 168 publications

References 25 publications

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

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

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour

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