I-Corps: Measuring thermal efficiency in buildings using a thermal energy imaging platform with AI and thermodynamic analysis

I-Corps：使用具有人工智能和热力学分析功能的热能成像平台测量建筑物的热效率

• 批准号: 2204689
• 负责人: Prakash Ranganathan
• 金额: $ 5万
• 依托单位: University of North Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204689&HistoricalAwards=false
• 关键词: Corps; Measuring; thermal; efficiency; buildings

The broader impact/commercial potential of this I-Corps project is the development of automation technology for energy assessment related to thermal efficiency. Aging infrastructure and insufficient weatherization measures have increased power and natural gas consumption levels and emissions. The best means of reigning in consumption rates are improved energy efficiency and weatherization enabled through proper quantified energy assessments. The proposed technology enables faster, safer, and more accurate thermal assessments for residential and commercial buildings, providing a data-rich source for identification of points of largest energy consumption and measures to improve thermal efficiency of structures. The primary market this technology is public and private educational institutions like higher-education universities and K-12 institutions. Facility and Plant Managers may be able to use the technology to rapidly identify measures to reduce utility charges and increase the lifespan of structures. The quantification of energy consumption also may enable these mangers to access further state or federal funding for structural improvements. In addition, the proposed technology may be useful for weatherization experts and structural engineers working on construction projects and aiding utility providers for energy infrastructure inspection. This I-Corps project is based on the development of a technology to quickly convert thermal images captured from a UAS mounted radiometric infrared sensor to energy utilization estimations using artificial intelligence, data analytics, and thermodynamics. The thermal images captured by the infrared sensors are corrected for atmospheric attenuation and processed by the artificial intelligence to identify individual components of a building’s façade and any thermal anomalies. Thermodynamic procedures are utilized to calculate the heat conductance or the U-value of each element and calculate the financial impact of heat loss from the building depending on the dimensions of the elements being considered along with the type of utility used for space heating/cooling. This technology solution is slated to automate thermal assessment processes for buildings and reduce the overall processing time by 90% and on-site inspection time by 60% compared to traditional assessment techniques. The initial research for the technology was performed over a three-year period where thermal images of various structures throughout North Dakota were analyzed and cross-referenced for their thermal profiles. The technology has since been improved by adding financial impact calculations and corresponding modeling tools along with a more structured data workflow process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该I-Corps项目的更广泛的影响/商业潜力是开发与热效率相关的能源评估的自动化技术。衰老的基础设施和不足的天气措施的功率和天然气消耗水平和排放量增加。统治消费率的最佳手段是通过适当的量化能源评估提高了能源效率和气候化。提出的技术可以为居民和商业建筑提供更快，更安全，更准确的热评估，从而为识别最大能源消耗点的数据提供了丰富的来源，以提高结构的热效率。该技术的主要市场是公立和私人教育机构，例如高等教育大学和K-12机构。设施和工厂经理可能能够使用该技术快速识别措施，以减少公用事业费用并提高结构的寿命。能源消耗的数量也可以使这些经理能够获得进一步的州或联邦资金以进行结构性改进。此外，该提议的技术可能对从事建筑项目和帮助实用程序提供商进行能源基础设施检查的实用程序提供商可能很有用。该I-Corps项目基于技术的开发，该技术使用人工智能，数据分析和热力学来快速将从UAS安装的辐射光红外传感器捕获的热图像为能量利用率估算。红外传感器捕获的热图像对于大气衰减是正确的，并由人工智能处理，以识别建筑物立面和任何热异常的单个组件。使用热力学程序来计算每个元素的热电导或U值，并根据所考虑的元素的尺寸以及用于空间加热/冷却的实用程序类型来计算建筑物的热量损失的财务影响。与传统评估技术相比，该技术解决方案计划用于建筑物的自动热评估过程，并将整体处理时间降低90％，现场检查时间降低60％。该技术的最初研究是在三年的时间内进行的，其中分析了整个北达科他州各种结构的热图像，并以其热轮廓进行了交叉引用。此后，通过添加财务影响计算和相应的建模工具以及更具结构化的数据工作流程来改进该技术。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，通过评估被认为是珍贵的支持。