Multi-objectives occupant-centric control of thermostats and natural ventilation systems in cold climate conditions using real-time occupant-related information

Recently, Occupant-Centric Controls (OCCs) have attracted great attention. Many studies have applied OCCs to mechanical ventilation systems, while natural ventilation systems have gathered much less attention. The natural ventilation effect, driven by opening windows or vents, can be effective in improving indoor air quality, however, result in a leakage of interior heating energy in winter in cold climates. Therefore, the controls of thermostats and natural ventilation systems shall be designed well so that satisfying levels of thermal comfort, air quality and energy conservation can be simultaneously guaranteed. This study proposes an OCC and applies it to thermostats and natural ventilation systems for indoor thermal comfort, air quality and heating energy management. The strategy comprises three controllers: occupancy-based on-off controller, controller of setpoint of indoor air temperature and window openness controller. First, real-time profiles of heat gains from occupants and window openness were collected by employing vision-based detection. Second, a parametric building simulation study was conducted, and then simulation data were generated to develop predictive shallow artificial neural networks for forecasting the indoor conditions and energy performance of the investigated room. Third, the performance of the proposed strategy was examined. Compared to the baselines, the control could offer a decrease in building heating loads by between 43.4%-63.8% and an improvement of predicted mean vote levels by 0.36-0.37. The proposed OCC could also maintain indoor CO2 concentrations below 1,000 ppm for about 91% of the studied time, while 84.2% of the time with several peaks over 3,000 ppm in the baseline cases.