Estimating surface temperature from thermal imagery of buildings for accurate thermal transmittance (U-value): A machine learning perspective
Estimating surface temperature from thermal imagery of buildings for accurate thermal transmittance (U-value): A machine learning perspective
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DOI：
10.1016/j.jobe.2020.101637
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发表时间：
2020-08
影响因子：
6.4
通讯作者：
Debanjan Sadhukhan;Sai Peri;Niroop Sugunaraj;Avhishek Biswas;D. Selvaraj;Katelyn Koiner;A. Rosener;Matt Dunlevy;Neena Goveas;D. Flynn;P. Ranganathan
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作者：
Debanjan Sadhukhan;Sai Peri;Niroop Sugunaraj;Avhishek Biswas;D. Selvaraj;Katelyn Koiner;A. Rosener;Matt Dunlevy;Neena Goveas;D. Flynn;P. Ranganathan
建筑物热性能评估是优化能源管理、热损失评估和能源审计应用的重要过程。这种评估有助于预见未来干预的要求，并有助于制定能源绩效基准。本文综述了几种热性能评估技术，并根据测量类型、方法和应用进行了广泛的分类。此外，本文还对建筑构件实际热损失评估所用的各种定量指标进行了全面的综述。本文的独特贡献是提出了三层框架，详细介绍了基于UAS的热图像热损失量化的处理和加工。首先，这项工作的新奇在于应用实例分割技术（Mask R-CNN）来计算热透射率值（例如，U值）用于各种对象（例如，门、墙、窗和立面）。据我们所知，这项研究工作是首次使用相当大的热数据库（例如100，000张增强图像）。分析了窗户和墙壁的多个标准U值，并与美国采暖、制冷和空调工程师协会（ASHRAE）建筑标准进行了比较。来自多个校园建筑的超过100，000个训练（包括增强）图像的Mask-RCNN的初步结果产生了以下性能指标：1）提供了0.67（窗户）和0.46（立面）的平均精度（AP）;以及2）分别为0.05（窗户）和0.5（立面）的交集（IoU）。此外，U值在区分窗户类型方面始终足够接近ASHRAE标准（例如，单窗格窗户为0.77，双窗格窗户为0.38）。
Thermal performance assessment of building(s) is an essential process for optimal energy management, heat-loss evaluation, and energy audit applications. Such an assessment can help foresee the requirements for future intervention(s) and aid in benchmarking energy performance. This paper provides a review of several thermal performance assessment techniques and a broad classification based on measurement types, methods, and applications. Moreover, the article provides a comprehensive survey of various quantitative indices utilized for practical heat-loss assessment of building elements. This paper’s unique contribution is the proposed three-layer framework that details the handling and processing of UAS-based thermal imagery for heat loss quantification. Primarily, the novelty of this work lies in the application of an instance segmentation technique (Mask R–CNN) to compute the thermal transmittance values (e.g., U-values) for various objects (e.g., doors, walls, windows, and facades). To the best of our knowledge, this research work is first-of-its-kind using a sizeable thermal data repository (e.g. 100,000 augmented images). Multiple standard U-values are analyzed for windows and walls and compared with The American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards. The preliminary results of Mask-RCNN from over 100,000 trained (including augmented) images from multiple campus buildings yield the following performance metrics: 1) provides an Average Precision (AP) of 0.67 (windows) and 0.46 (facades); and 2) Intersection of Union (IoU) of 0.05 (windows) and 0.5 (facades) respectively. Moreover, the U-values are consistently close enough to the ASHRAE standards in distinguishing window types (e.g. 0.77 for single-pane windows and 0.38 for double-pane windows).