Real energy performances of buildings depend not only on deterministic aspects, such as building physics and HVAC systems, but also on stochastic aspects such as weather and occupants' behavior. Typically, occupant behavior is not adequately considered when calculating the expected performance. As a result, field test studies all over Europe have shown discrepancies between real and expected energy performance of buildings. In order to bridge this gap, stochastic occupants' behavior models could be embedded into building energy performance simulation software. In order to make such models, there is a need for a better understanding of occupants' behavior and in particular the reasons of their adjustments of building controls such as window opening, heating set points, etc. The purpose of this paper was to analyze window opening behavior in residential buildings, investigate which drivers lead occupants to interact with windows and how these actions can be modeled. A method to analyze the probability of a state change of the windows, based on logistic regression, was applied to monitored data (measured each minute) from two refurbished demonstration buildings. The weather and the five rooms of the 60 apartments located in the buildings were monitored in terms of air quality and thermal environment (presence of occupants was not monitored) during four years.The most common driver to open a window was the time of the day, followed by the carbon dioxide concentration. The most common driver to close a window was the daily average outdoor temperature, followed by the time of the day. (C) 2016 Elsevier Ltd. All rights reserved.
- Logistic regression
- Natural ventilation
- Buildings' energy performance
- Building energy performance simulation
- Case study
- Field test