Integration of Statistical Models of Residential HVAC Loads with a Commercial Smart Thermostat

As part of an effort to develop accurate power flow simulations in the area of Demand Response (DR) control, we developed an agent-based model for power consumption by residential end uses. Electrical power loads occurring in individual houses are categorized and modeled statistically. We developed a thermostat model to simulate the HVAC power draw, one of the most important residential load categories. In the present work, we replace the simulated thermostat from one of the house models participating in the aggregated load control, with a physical instance of commercial smart thermostat. Specifically, we selected a 'Nest Learning Thermostat' for integration in the load simulation. We used the Nest Application Programming Interface (API) for the integration process. We implemented a PID control system to regulate the temperature of an environmental chamber where the Nest thermostat is installed. The environmental chamber is intended to provide the Nest with conditions similar to what it would experience in a real-life setting. Learnings from the present work will serve to increase the realism of large-scale agent-based simulations.