Model predictive building thermostatic controls of small-to-medium commercial buildings for optimal peak load reduction incorporating dynamic human comfort models: Algorithm and implementation

The peak kW of a typical New York State office building is thought to primarily be a function of the HVAC system, often the buildings largest load, but may also be influenced by occupancy and other loads. First, a simple lumped parameter model with a minimum amount of building´s physical input data, and trained with actual thermal and electrical data, is considered to approximate the thermal/electric consumption performance of the building and HVAC system on a zonal basis. Then, the lumped parameter model integrated with a dynamic human comfort model is used to develop optimized zonal thermostat setpoint schedules to minimize the cooling systems contribution to the buildings peak power load while maintaining human comfort at a desired level. A 24-hour weather and occupancy forecasts are also incorporated into the optimization algorithm. The key difference of our approach compared to previous approaches that utilize model-predictive control is that a minimal set of measurement profiles are utilized to reduce the installation cost resulting in a cost effective advanced controls solution for a large number of small and medium size office buildings. The model predictive optimization approach is implemented at multiple demonstration sites. The hardware architecture and software platform installed at one of the demonstration buildings are discussed. Finally, it is demonstrated that the proposed controller can effectively minimize peak cooling load on the HVAC equipment while achieving a satisfactory thermal comfort inside the building.