Automated Diagnostics and Visualization of Potential Energy Performance Problems in Existing Buildings Using Energy Performance Augmented Reality Models

Abstract

Quick and reliable identification of energy performance problems in buildings is a critical step in improving their efficiency. The current practice of building diagnostics typically involves nonintrusive data collection using thermal cameras. This requires large amounts of unordered and nongeo-tagged two-dimensional (2D) imagery to be manually analyzed at a later stage, which makes the analysis time-consuming and labor-intensive. Because of the absence of a benchmark for energy performance, identification of performance problems also heavily relies on the auditor’s knowledge, and consequently may lead to subjective and error-prone inspections. As a step towards rapid and objective identification of performance problems, this paper presents a new method for automated analysis and visualization of deviations between buildings’ actual and simulated energy performances. The proposed method is based on the recently developed energy performance augmented reality (EPAR) environments. In the EPAR modeling method, actual and expected three-dimensional (3D) spatio-thermal models are generated and superimposed in a common 3D virtual environment. The method leverages unordered collections of thermal and digital imagery for actual energy performance modeling, in addition to computational fluid dynamics (CFD) analysis for expected energy performance simulation. Based on the EPAR models which store actual and simulated thermal values at the level of 3D points, two new algorithms are developed to facilitate identification of potential performance problems: (1) 3D thermal mesh modeling using