A Power Disaggregation Approach to Identify Power-Temperature Models of HVAC Units

With the increasing deployment of smart meters and Internet-of-Things technologies, buildings are becoming smarter, and capable of participating in demand response (DR) programs. Heating, Ventilation and Air Conditioning (HVAC) units, as one of the major loads in buildings and due to their cyclic operating characteristics, are excellent candidates for peak load management. A prerequisite to HVAC control for peak load management is to know their power consumption. While many studies assume that HVAC units consume fixed rated power during their operation, field measurements show that this value is not fixed but varies with outdoor temperature. Performing DR using the fixed power consumption model may result in unexpected load reduction, i.e., the reduction may be lower or higher than expected. Although power consumption measurements of individual HVAC units can provide necessary data, it is prohibitively expensive to install a power meter for each HVAC unit. To reduce hardware investments, this paper proposes an algorithm to derive power-temperature models of individual HVAC units from a single power meter that measures the power consumption of all HVAC units. Research findings indicate that the power consumption of individual HVAC units can be precisely modeled and disaggregated from a single power meter data.