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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. [ABSTRACT FROM AUTHOR]

Using extensive field survey data acquired over the past 8 years at the Solar Energy and Building Physics Laboratory at EPFL in Switzerland, comprehensive models of occupants' presence, opening and closing of windows and the raising and lowering of blinds have been developed. These new models have been integrated within a new urban energy modelling tool, called CitySim. In this article, we describe briefly the structure of CitySim together with the means for representing occupants' presence and behaviour, both deterministic and stochastic. For a hypothetical scenario, we then go on to present results from simulations of the impact that occupants' behaviour may have on the indoor environment in buildings as well as on buildings' energy demands. From this we conclude that occupants' behaviour has a significant impact (of the order of a factor of two) on buildings' energy demands and that individuals' diversity has a yet greater impact. [ABSTRACT FROM PUBLISHER]

Based on results from a field survey campaign conducted in Switzerand, we show that occupants' variations in clothing choices, which are relatively unconstrained, are best described by the daily mean outdoor temperature and that major clothing adjustments occur rarely during the day. We then develop an ordinal logistic model of the probability distribution of discretised clothing levels, which results in a concise and informative expression of occupants' clothing choices. Results from both cross-validation and independent verification suggest that this model formulation may be used with confidence. Furthermore, the form of the model is readily generalisable, given the requisite calibration data, to environments where dress codes are more specific. We also observe that, for these building occupants, the prevailing metabolic activity levels are mostly constant for the whole range of surveyed environmental conditions, as their activities are relatively constrained by the tasks in hand. Occupants may compensate for this constraint, however, through the consumption of cold and hot drinks, with corresponding impacts on metabolic heat production. Indeed, cold drink consumption was found to be highly correlated with indoor thermal conditions, whilst hot drink consumption is best described by a seasonal variable. These variables can be used for predictive purposes using binary logistic models. [ABSTRACT FROM AUTHOR]

Abstract: During the warm summer of 2006 a comprehensive longitudinal field survey of the adaptive actions of occupants, their thermal satisfaction and the coincident environmental conditions was conducted in eight Swiss office buildings. We have applied logistic regression techniques to these results to predict the probability of occupants’ actions to adapt both personal (clothing, activity and drinking) and environmental (windows, doors, fans and blinds) characteristics. We have also identified, for each type of control action exercised, the increases in temperature at which thermal sensation votes are reported. These “empirical adaptive increments” have also been defined for combinations of control action. In this paper we present the field survey methodology as well as the results relating to the above, which we discuss along with scope for further related work. [Copyright &y& Elsevier]