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首页> 外文期刊>Journal of Computing in Civil Engineering >Automated Diagnostics and Visualization of Potential Energy Performance Problems in Existing Buildings Using Energy Performance Augmented Reality Models
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Automated Diagnostics and Visualization of Potential Energy Performance Problems in Existing Buildings Using Energy Performance Augmented Reality Models

机译：使用能源绩效增强现实模型对现有建筑物中潜在的能源绩效问题进行自动诊断和可视化

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摘要

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 threes-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 k-d trees and nearest-neighbor searching to automate calculation of temperature deviations, and (2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed modeling method is validated on several interior locations of instructional and residential buildings. Empirical observations show that automated analysis using EPAR models enables performance deviations to be rapidly and accurately measured. The visualization of deviations in three dimensions enables auditors to easily identify potential performance problems and, in turn, enables auditors to focus more on other important tasks of analyzing retrofit alternatives.
机译：快速可靠地识别建筑物中的能源性能问题是提高其效率的关键步骤。当前建筑诊断的实践通常涉及使用热像仪进行非侵入式数据收集。这就需要在以后的阶段手动分析大量无序且没有地理标签的二维（2D）图像，这使分析既费时又费力。由于缺乏能效基准，性能问题的识别也严重依赖审核员的知识，因此可能导致主观检查和容易出错的检查。为了快速，客观地识别性能问题，本文提出了一种自动分析的新方法-可视化建筑物实际和模拟能耗之间的偏差。所提出的方法基于最近开发的能源性能增强现实（EPAR）环境。在EPAR建模方法中，将生成实际的和预期的三维（3D）时空热模型，并将其叠加在常见的3D虚拟环境中。除了用于预期能源性能模拟的计算流体动力学（CFD）分析之外，该方法还利用热图像和数字图像的无序集合进行实际能源性能建模。基于在3D点级别存储实际和模拟热值的EPAR模型，开发了两种新算法来促进潜在性能问题的识别：（1）使用kd树和最近邻搜索进行3D热网格建模以自动计算温度偏差；以及（2）使用基于交通灯颜色的隐喻自动可视化性能偏差。所提出的建模方法在教学楼和住宅楼的多个内部位置得到了验证。经验观察表明，使用EPAR模型进行的自动分析可以迅速而准确地测量性能偏差。可视化三个方面的偏差，使审核员可以轻松识别潜在的性能问题，进而使审核员可以将更多精力放在分析改造方案的其他重要任务上。

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