Machine learning

Data from mechanical extract ventilation units of Renson Ventilation nv installed in Belgium is utilized to detect space occupancy through machine learning. Challenges with the detection of occupancy using data captured by these smart devices are (1) absence of labelled data for training a machine learning model, and (2) occupant’s CO2 generation rate and building layouts influence the measured CO2 concentrations, which prevents simple rule-based models to be used for data labelling.

Thermal comfort and sensation are important aspects of the building design and indoor climate control as modern man spends most of the day indoors. Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that views the building occupants as passive recipients of their thermal environment. Assuming that people have relatively constant range of biological comfort requirements, and that the indoor environmental variables should be controlled to conform to that constant range.

Occupants control indoor environments to meet their individual needs for comfort. The control of window is the most common natural ventilation method influencing indoor environment as well as the energy use of the buildings to maintain a suitable environment. Therefore a better understanding of window control behaviour of the occupants has significant implication to enhance occupant comfort with minimal energy consumption. The objective of this study was to identify an appropriate algorithm and variables to develop a predictive model for window control.