E3S Web Conf.
Volume 111, 2019CLIMA 2019 Congress
|Number of page(s)||8|
|Section||High Energy Performance and Sustainable Buildings, Simulation models and predictive tools for the buildings HVAC, IEQ and energy|
|Published online||13 August 2019|
Inclusion of window opening habits in a window model based on activity and occupancy patterns
Ghent University, Faculty of Engineering and Architecture, Research Group Building Physics, 9000, Gent, Belgium
* Corresponding author: firstname.lastname@example.org
The occupants’ window opening behaviour can have a substantial influence on the indoor climate and the energy use in low energy dwellings. In literature, most window opening models are based on outdoor and/or indoor climate variables. However a study of Verbruggen et al.  revealed that these models are not able to predict the window opening behaviour accurately in wintertime, which may be attributed to the presence of window opening habits. The occupants perform the habits not according to a fixed time step but rather to the performance of a reoccurring activity or an occupancy change. Therefore, a window opening model is generated based on the occupancy and activity patterns of the inhabitants. The model links certain behaviours to specific activities or moments in an occupant’s day without relating it to an exact time-step or specific weather conditions. Data on these habits and the links with occupancy are acquired from a survey conducted in a NZEB case-study project in Belgium. This paper includes the results of the habit-survey and explains how the window use model based on habits is generated. Based on the answers from the survey the window use in bedrooms and bathrooms could be fully defined for 93% of the households, only in the living room no complete window use profile could be defined. The developed model is able to predict the window use in a more realistic way compared to weather-models, with window opening actions linked to specific moments in the occupant’s day.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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