LAUSR

For demand-side management programs concerned with heating, ventilation, and air conditioning (HVAC) energy consumption, smart meter data collected at the whole-premise level has recently been used to decompose usage into its HVAC and non-thermal components, which are typically not separately monitored. In this paper, we study the extent to which program design and decisions based on models using whole-home energy consumption differ from decisions made with full knowledge of appliance-level end-use patterns. We develop a model assessment methodology for the case when model results are used to rank consumers by their potential for thermal demand-response. For this, we compare rankings of consumers in two scenarios—first, when only the aggregate outcome of the top consumers matters, then when the relative ordering of the consumers is important. We illustrate our methodology using two individual consumption models that extract thermal (temperature-sensitive) and occupant-driven components from single-point source smart meter data. Moreover, we discuss how a demand response program that selects the consumers with the most potential for energy reduction based on model results may achieve similar results as in the ideal case when separately monitored HVAC data is used.