Analysis of occupants' behavior related to the use of windows in German households. (July 2016)

• Record Type: Journal Article
• Authors: Calì, Davide; Andersen, Rune Korsholm; Müller, Dirk; Olesen, Bjarne W.
• Is Part Of: Building and environment. Volume 103(2016)
• Page Start: 54
• Page End: 69
• Publication Date: 2016-07
• Subjects: Logistic regression -- Natural ventilation -- Buildings' energy performance -- Building energy performance simulation -- Case study -- Field test; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2016.03.024
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: 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. Highlights: Occupants open and close windows depending on drivers leading them to take action. Drivers can be identified through logistic regression analysis. The most common drivers for opening action are: time of the day and CO2 concentration. The most common drivers for closing action are: outdoor temperature and time of the day. Thermal comfort and AIQ play a role for occupant behavior in regard to natural ventilation.