Stochastic models for window opening and air-conditioning usage in mixed-mode offices for a humid subtropical climate in Brazil

• Published in: Building and environment 2022-11, Vol.225, p.109579, Article 109579
• Main Authors: Grassi, Camila, Chvatal, Karin Maria Soares, Schweiker, Marcel
• Format: Article
• Language: English
• Subjects: Humid subtropical climate; Mixed-mode ventilation; Occupant behavior; Office buildings; Statistical modeling
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2022.109579

The way buildings are operated highly influences energy consumption, and occupants play a significant role in it. However, challenges related to the prediction of occupants’ behavior decrease the potential to accurately predict a building’s energy consumption. Such inaccuracy presents one reason for the gap between measured and simulated energy data. In an effort to bridge this gap, this work aims at creating statistical models to predict occupant behavior, in relation to window and air conditioning operation in mixed-mode office buildings in a humid subtropical climate. The statistical behavioral models developed were based on data collected in an 18-month monitoring campaign conducted in São Carlos, Brazil. Two types of statistical methods were applied: generalized linear mixed and Markov chain models. Model results showed high probabilities of status change in both controls upon arrival and departure. Indoor temperature approximately at 24 °C and outdoor temperature around 23 °C show the lowest probability of either control being activated. In addition, the behavioral patterns presented by the models show an alternating use of the controls, indicating a conscious use of the available equipment. •Stochastic models to predict occupant behavior in mixed-mode office buildings.•Prediction of air-conditioning and window operation entirely controlled by users.•Analysis of occupant behavior data in a humid subtropical climate for all seasons.•Novel observations about activation time and alternating use of AC and windows.•Innovative contributions on occupant behavior in mixed-mode office buildings.