Open AccessA Probabilistic Prediction Model for Window Opening during Transition Seasons in Office Building
Author(s) -
Jing Liu,
Runming Yao,
Rechel McCloy
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/310/2/022017
Subject(s) - window (computing) , thermal comfort , indoor air quality , probabilistic logic , environmental science , architectural engineering , air temperature , indoor air , air change , efficient energy use , computer science , meteorology , simulation , engineering , environmental engineering , artificial intelligence , geography , ventilation (architecture) , operating system , electrical engineering
Window operation of occupants in building has close relationship with indoor air quality, indoor thermal environment and building energy performance. The objective of this study was to understand occupants’ interaction with window opening in transition seasons considering the influence of subject type (e.g. active and passive respondents) and to develop corresponding predictive models. An investigation was carried out in non-air-conditioned building in the UK covering the period from September to November. Outdoor temperature in this study was determined as good predictor for window operation. The differences in window opening probabilities between active and passive subjects were significant. Active occupants preferred to open window for fresh air or for indoor thermal condition adjustment, even though the outdoor air temperature sometimes were less than 12 °C. Proper utilization of windows in transition seasons contributed significantly to building energy saving and further improve energy efficiency in buildings.