Calibrating physical parameters in house models using aggregate AC power demand

For residential houses, the air conditioning (AC) units are a major resource for providing flexibility in energy use for the purpose of demand response. To quantify the flexibility, the characteristics of the population of houses need to be accurately estimated, so that models of house energy-use can be used to predict the temperature dynamics. By adjusting the house thermostat setpoints accordingly, comfort can be maintained and demand response is possible. In this paper, we propose an approach using the Reverse Monte Carlo method and aggregate house models to calibrate the (probability) distribution parameters of the house models for a population of residential homes. Given the aggregate AC power demand for the population, our approach can successfully estimate the distribution parameters for the most sensitive physical parameters identified in previous studies, such as the mean floor area for the population of houses. Moreover, we give uncertainty bounds for our parameter estimates
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