Automated cost analysis of energy loss in existing buildings through thermographic inspections AND CFD analysis
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Understanding energy performance of existing buildings is vital to increasing their efficiency and reducing the overall energy consumptions. This entails facility managers to systematically monitor building energy performance and reliably identify and analyze potential problems. Currently, infrared thermography is widely used as a primary diagnostic tool for the detection of building performance problems. Nonetheless, applications of thermal images for building inspection are mainly restricted to manual and labor-intensive identification and qualitative assessment of heating or cooling loss. Automated identification of potential problems and reliable cost analysis of the associated energy loss can help homeowners to minimize financial risk of retrofits and maximize energy savings. To that end, this paper presents a new automated method for calculating the cost of energy loss for building diagnostics. In the proposed method, first, using a hand-held thermal camera, the auditors collect digital and thermal imagery from the buildings under inspection. Then, using a recently proposed method for Energy Performance Augmented Reality (EPAR) modeling, an actual 3D spatio-thermal model is generated and superimposed with a computational fluid dynamics (CFD)-based expected energy performance model. The resulting EPAR model is placed into the method proposed in this paper for cost analysis of the energy loss. Through a new 3D thermal mesh modeling using k-d tree structure and nearest neighborhood searching, performance deviations between these models are automatically calculated. Using a temperature threshold, the areas associated with potential performance problems are detected in the EPAR model and are visualized using a metaphor based on traffic light colors. Then, the actual R-values of the detected areas are measured at the level of 3D points. Based on the measured R-values and the estimated air change rate for the detected air leaks, (1) the heat loss or gain caused either by poor insulation or air infiltration/ exfiltration and (2) the associated energy costs are automatically calculated. The proposed method is validated on several locations of existing residential buildings. The preliminary results show the potential of the proposed method for minimizing the inspection time as well as the risk associated with the cost analysis of retrofitting potential building performance problems.