Verification of stochastic models of window opening behaviour for residential buildings

Abstract

Based on the analyses of data from two distinct measurement campaigns conducted in residential indoor environments in Japan and Switzerland, we identify the specificities of occupants' behaviour with respect to their interactions with windows, including the choice of opening angles for axial openings. As a first step, each dataset is analysed to develop separate predictive models which account for the specificities of window usage in the residential context. The predictive accuracy of these models is then challenged by validation on external data: using models inferred from data obtained from one survey, actions on windows are simulated for the other survey and the predictions are compared with observations. Dynamic models developed using data from office buildings as well as previously published models are also compared using this verification procedure. In the case of the Swiss dataset, these analyses demonstrate the ability of carefully formulated behavioural models developed from office environment data to reliably predict window usage in a residential context and vice-versa. However, we observe that the same models perform less satisfactorily in the prediction of window usage in Japan. From these results it seems that such models require specific calibration in the case of buildings equipped with an air-conditioning unit as was the case for the hot and humid summer climate of Japan.

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

“…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). It also includes the use of services within the building, such as hot water, cooking and electrical appliances, which consume energy and generate internal heat gains (Isaksson and Karlsson 2006;Yamaguchi, Fujimoto, and Shimoda 2011).…”

Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour

“…• Existing comprehensive stochastic models for window opening and solar shading control in a residential environment are not sufficiently described or validated, in contrast to the models for offices (Warren & Parkins, 1984;Fritsch et al , 1990;Johnson & Long, 2005;Rijal et al , 2008Rijal et al , , 2011Yun & Steemers, 2008;Yun et al , 2009;Yun & Steemers, 2010;Haldi & Robinson, 2011;Schweiker et al , 2012;Andersen et al , 2013;Yun et al , 2009;Guerra-santin & Itard, 2010;Haldi & Robinson, 2011). …”

On the multi-agent stochastic simulation of occupants in buildings

“…Haldi and Robinson (2009) compared twelve window opening models and found the most effective at predicting window openings in offices to be a hybrid model, this was later validated against residential buildings Schweiker et al (2012). This hybrid model first predicts transitions in opening status using a presence-dependent Markov chain and then in the cases of transitions to the open state, predicts the duration for which the window stays open using a Weibull distribution.…”

“…Upon opening survival duration is calculated ( EnergyPlus an external schedule for windows is created, setting the value to be either 1 for fully open or 0 for fully closed for each time step (in the future it would be useful to include predictions of opening proportion, based for example on the model described in Schweiker et al (2012)). …”