Development of datasets, inverse models, and methods for adaptive fault detection and diagnostics in commercial buildings

开发商业建筑自适应故障检测和诊断的数据集、逆模型和方法

• 批准号: RGPIN-2017-06317
• 负责人: Gunay, Burak
• 金额: $ 4.23万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749052
• 关键词: Development; datasets; inverse; models; methods

30 to 50% of the energy use in commercial buildings is wasted due to poorly maintained, degraded, and improperly controlled equipment and components. Given that indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2 emissions in Canada, optimizing their operation represents great potential to reduce our environmental impact and to provide comfortable, healthy, and productive indoor environments. The overall objective of this research program is to optimize the energy use and occupant comfort in commercial buildings by using sensor, meter, and actuator data gathered in modern building automation and control systems. With this vision in mind, the research program will address four fundamental gaps in the literature: (1) create a dataset comprising common building faults, their sensory symptoms, occurrence frequencies, and impact on energy use and comfort; (2) develop inverse models that explain multiphysical processes and occupant behaviour in buildings from sensor, meter, and actuator data; (3) develop scalable methods to diagnose physical faults in building systems and components; and (4) develop scalable methods to diagnose and correct soft faults in controls programming. The research approaches entail field-scale data collection and analyses using existing controls and automation infrastructure of three office buildings in Carleton University, field trials, and building performance simulation. The proposed research program will make significant short-term and long-term intellectual, environmental, economic, and HQP contributions to Canada. New datasets, models, and methods will be created. These will help us understand how our buildings perform and are used today. Wider usage of fault detection and diagnostics methods developed in this research program will reduce the environmental and economic impact of buildings. Adoption of these methods by a Canadian building data analytics company will contribute to our knowledge-based economy. More importantly, two PhD, two MSc, and three undergraduate students will work on data from real buildings, learn their systems and components, and their shortcomings. In a team environment, they will conduct interdisciplinary research on building physics, indoor environmental quality, building performance simulation, and data-science. These skills are invaluable, as few engineering programs in Canada provide a comprehensive background in building engineering, despite buildings' major role in our economy and society.

商业建筑中 30% 至 50% 的能源使用因设备和组件维护不善、性能退化和控制不当而被浪费。鉴于商业建筑的室内气候控制占加拿大总能源消耗的 13% 和二氧化碳排放量的 11%，优化其运行对于减少对环境的影响并提供舒适、健康和高效的室内环境具有巨大潜力。该研究计划的总体目标是通过使用现代建筑自动化和控制系统中收集的传感器、仪表和执行器数据来优化商业建筑的能源使用和居住舒适度。考虑到这一愿景，该研究计划将解决文献中的四个基本空白：（1）创建一个数据集，其中包括常见的建筑故障、其感官症状、发生频率以及对能源使用和舒适度的影响； (2) 开发逆模型，根据传感器、仪表和执行器数据解释建筑物中的多物理过程和居住者行为； (3) 开发可扩展的方法来诊断建筑系统和组件的物理故障； (4) 开发可扩展的方法来诊断和纠正控制编程中的软故障。研究方法包括使用卡尔顿大学三座办公楼的现有控制和自动化基础设施进行现场规模数据收集和分析、现场试验和建筑性能模拟。拟议的研究计划将为加拿大做出重大的短期和长期智力、环境、经济和总部计划贡献。将创建新的数据集、模型和方法。这些将帮助我们了解我们的建筑如今的性能和使用情况。本研究项目中开发的故障检测和诊断方法的更广泛使用将减少建筑物对环境和经济的影响。加拿大建筑数据分析公司采用这些方法将为我们的知识经济做出贡献。更重要的是，两名博士生、两名硕士生和三名本科生将研究来自真实建筑的数据，了解它们的系统和组件以及它们的缺点。在团队环境中，他们将进行建筑物理、室内环境质量、建筑性能模拟和数据科学等跨学科研究。这些技能非常宝贵，因为尽管建筑在我们的经济和社会中发挥着重要作用，但加拿大很少有工程项目提供建筑工程的全面背景。