Energy efficiency programs implemented by utilities in the U.S. have rendered savings costing on average $0.03/kWh. This cost is still well below energy generation costs. However, as the lowest cost energy efficiency measures are adopted, the cost effectiveness of further investment declines. Thus, there is a need to develop large-scale and relatively inexpensive energy auditing techniques to more efficiently find opportunities for savings. Currently, on-site building energy audits process are expensive, in the range of US$0.12/sf - $0.53/sf, and there is an insufficient number of professionals to perform the audits. Here we present research that addresses at community-wide scales the characterization of building envelope thermal characteristics via drive-by and fly-over GPS linked thermal imaging. A central question drives this research: Can single point-in-time thermal images be used to infer R-values and thermal capacitances of walls and roofs? Previous efforts to use thermal images to estimate R-values have been limited to stable exterior weather conditions. The approach posed here is based upon the development of a dynamic model of a building envelope component with unknown R-value and thermal capacitance. The weather conditions prior to the thermal image are used as inputs to the model. The model is solved to determine the exterior surface temperature, ultimately predicted the temperature at the thermal measurement time. The model R-value and thermal capacitance are tuned to force the error between the predicted surface temperature and the measured surface temperature from thermal imaging to be near zero. The results show that this methodology is capable of accurately estimating envelope thermal characteristics over a realistic spectrum of envelope R-values and thermal capacitance present in buildings nationally. With an assumed thermal image accuracy, thermal characteristics are predicted with a maximum error of respectively 20% and 14% for high and low R-values when the standard deviation of outside temperature over the previous 48 hours is as much as 5°C. Experimental validation on a test facility with variable surface materials was attempted under variable weather conditions, e.g., where the outdoor air temperature experiences varying fluctuations prior to imaging. The experimental validation realized errors less than 20% in predicting the R-value even when the standard deviation of outdoor temperature over the 48 hours prior to a measurement was approximately 5°C.
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