Calculating the cost of heating and cooling loss for building diagnostics using EPAR (Energy Performance Augmented Reality Models)
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Successful building diagnostics requires quick and reliable identification of energy performance problems, and an accurate assessment and cost analysis of the associated energy loss. Today, most auditors rely on thermal imagery for analyzing energy performance problems. Nonetheless, current thermographic inspections are qualitative and thus mainly focus on the visual detection of thermal irregularities. As a result, the quality of the retrofit decision-making is based on how these images are interpreted by the auditors. To overcome these challenges, this paper presents a new method for calculating the cost associated with potential building energy performance problems. First, based on the Energy Performance Augmented Reality (EPAR) modeling method, based on the Energy Performance Augmented Reality (EPAR) modeling method, based on the Energy Performance Augmented Reality (EPAR) modeling method, actual and expected 3D spatio-thermal models of the buildings under inspection are generated and integrated in a 3D environment. This method leverages thermal and digital images captured by a thermal camera for actual energy performance modeling, in addition to computational fluid dynamics (CFD) analysis for expected energy performance simulation. Based on thermal 3D mesh modeling and threshold-based assessments of temperature deviations, building areas with potential performance problems are detected within the EPAR models. By locating a small crumbled aluminum foil on an inspection surface, the reflected apparent temperature is measured. For areas with potential performance problems within EPAR models, this measurement is used to calculate the actual R-value at the level of 3D points. This information is finally used to calculate both heat loss and gain and also to estimate the associated energy costs. The presented method is tested on several interior locations of an existing residential building. Experimental results and the benefits of converting temperature data sensed from building surfaces into actual R-values and the cost of heating and cooling loss are discussed. 2013 American Society of Civil Engineers.