Abstract
Operable windows have become desirable design features of modern mechanically ventilated office buildings in North America. While they improve perceived control and adaptive comfort, their inappropriate use poses risks associated with increased heating and cooling energy use. Therefore, the sequence of operations for terminal devices serving zones with operable windows should be designed in recognition of these risks, which in turn should be informed by research investigating occupants’ window and thermostat use behavior. To this end, this paper examines window and thermostat use data collected from two mixed-mode ventilation buildings in Ottawa, Canada. Discrete-time Markov logistic regression models and decision tree models were established to predict the likelihood of thermostat keypress and window opening/closing instances and identify the indoor conditions that trigger these actions. Based on this analysis, a set of preliminary recommendations is developed to improve terminal device sequencing in mixed-mode ventilation buildings in cold climates such that the comfort and energy savings potential of operable windows can be fully realized. The recommendations include applying thermostat setpoint setback to encourage occupants to open windows when conditions are advantageous for saving energy and discourage occupants from opening windows when energy penalties may be caused.
Acknowledgement
This research is supported by research funding provided by the National Research Council Canada. The authors are grateful for the technical support from Delta Controls and FMP Service Centre. We also acknowledge the IEA-EBC 79 researchers, as we have greatly benefitted from the associated discussions.
Additional information
Notes on contributors
Weihao Liu
Weihao Liu, B.Eng., M.A.Sc. Student, Student Member of ASHRAE, is a Graduate Research Assistant.
H. Burak Gunay
H. Burak Gunay, Ph.D. P.Eng., Associate Member ASHRAE, is an Assistant Professor.
Mohamed M. Ouf
Mohamed M. Ouf, Ph.D., P.Eng.,Associate Member ASHRAE, is an Assistant Professor.