Optimization of the design and operation of sustainable and resilient buildings of 21st century

21 世纪可持续和弹性建筑的设计和运营优化

• 批准号: RGPIN-2021-04030
• 负责人: Zmeureanu, Radu
• 金额: $ 2.33万
• 依托单位: Concordia 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=746405
• 关键词: Optimization; design; operation; sustainable; resilient

The long-term objective of the applicant's highly original and innovative research program is the optimization of design and operation of sustainable and resilient buildings of 21st century. Resilience is the process of adapting well in the face of adversity, trauma, tragedy, threats etc. and recovering quickly from such situations. Short-term objective 1 focuses on the development of a method for Automated Detection and Diagnosis of Simultaneous Multiple Faults (ADDSMF) of heating, ventilation and air conditioning (HVAC) systems. In the applicant's vision, Building Automation Systems (BAS) installed in commercial and institutional buildings should mimic the human nervous system, with some enhanced features such as data validation, knowledge extraction, and fault detection and diagnosis (FDD). The development of such a method is based on quality of measurements from BAS, and efficient adaptive mathematical models that learn continuously from new measurements to predict the performance benchmarks, detect simultaneous faults in HVAC systems, identify and self-calibrate the faulty sensors. The research program focuses on the application of Machine Learning (ML) techniques, a subfield of artificial intelligence (AI) domain, for the development of such adaptive models. Two different classes of models are proposed: Black-box models and Gray-box models. A prototype library of ADDSMSF models will be developed. The use of such models in commercial buildings will contribute to fast detection of faults, and reduction of operation costs and propagation of viruses in buildings. The models should be integrated in BAS produced by Canadian companies, which will become more competitive, nationally and internationally. Short-term objective 2 focuses on the development of multi-objective optimization framework of HVAC systems, with application to optimum selection of reference HVAC systems of National Energy Code of Canada for Buildings (NECB). The applicant proposes the development of a large-scale multi-objective optimization framework for the selection of configuration of HVAC systems and sizing the equipment capacity. The framework will be connected with a building energy analysis program (e.g., EnergyPlus, eQUEST). This is a problem of searching for minimum life cycle energy use, life cycle cost, life cycle exergy destruction, and life cycle environmental impact, four conflicting objective functions. Design alternatives of HVAC systems that cannot protect the occupants against anticipated outdoor threats (physical, chemical and biological) are eliminated. The proposed optimization framework will be used for the selection of innovative reference HVAC systems with low energy target value, for different building types in major cities in Canada. Outcomes of this research contribute to the update of NECB.

申请人高度原始和创新的研究计划的长期目标是对21世纪可持续和弹性建筑的设计和运营的优化。韧性是面对逆境，创伤，悲剧，威胁等很好地适应的过程，并从这种情况下迅速恢复。 短期目标1的重点是开发一种自动检测和诊断加热，通风和空调（HVAC）系统的多个故障（ADDSMF）的方法。在申请人的愿景中，安装在商业和机构建筑物中的建筑自动化系统（BAS）应模仿人类神经系统，并具有一些增强的功能，例如数据验证，知识提取以及故障检测和诊断（FDD）。这种方法的开发基于BAS和有效的自适应数学模型的测量质量，这些模型从新的测量值中不断学习以预测性能基准，检测HVAC系统中的同时故障，识别和自我校准错误的传感器。该研究计划的重点是机器学习（ML）技术的应用，即人工智能（AI）领域的一个用于开发这种自适应模型的应用。提出了两种不同类别的模型：黑框模型和灰色框模型。将开发ADDSMF模型的原型库。在商业建筑中使用此类模型将有助于快速发现故障，并降低了建筑物中病毒的运营成本和传播。这些模型应集成到加拿大公司生产的BAS中，该公司将在国内和国际上变得更具竞争力。短期目标2的重点是开发HVAC系统的多目标优化框架，并应用了加拿大国家能源法规的HVAC系统的最佳选择（NECB）。申请人建议开发一个大规模的多目标优化框架，以选择HVAC系统配置并尺寸设备容量尺寸。该框架将与建筑能源分析计划（例如，能量plus，equest）相连。这是寻找最小生命周期的能源使用，生命周期成本，生命周期破坏和生命周期环境影响，四个相互矛盾的目标功能的问题。无法保护乘员免受预期的室外威胁（物理，化学和生物学）的设计替代方案。所提出的优化框架将用于选择具有低能目标价值的创新参考HVAC系统，用于加拿大主要城市的不同建筑类型。这项研究的结果有助于更新NECB。