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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742826
• 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.

申请人高度原创和创新的研究项目的长期目标是优化21世纪可持续和弹性建筑的设计和运营。适应力是在面对逆境、创伤、悲剧、威胁等情况时能够很好地适应并迅速从这些情况中恢复的过程。短期目标1侧重于开发供暖、通风和空调（HVAC）系统同时多故障（ADDSMF）的自动检测和诊断方法。在申请人的构想中，安装在商业及机构楼宇内的楼宇自动化系统（BAS）应模仿人类神经系统，并具备一些增强功能，例如数据验证、知识提取和故障检测及诊断。这种方法的发展是基于BAS的测量质量，以及有效的自适应数学模型，该模型可以从新的测量中不断学习，以预测性能基准，检测HVAC系统中的同步故障，识别和自校准故障传感器。该研究计划的重点是应用机器学习（ML）技术，这是人工智能（AI）领域的一个子领域，用于开发这种自适应模型。提出了两类不同的模型：黑盒模型和灰盒模型。将开发一个ADDSMSF模型的原型库。在商业建筑中使用这些模型将有助于快速发现故障，降低运营成本和病毒在建筑物中的传播。这些模型应该被整合到加拿大公司生产的BAS中，这将在国内和国际上更具竞争力。短期目标2侧重于暖通空调系统多目标优化框架的开发，并应用于加拿大国家建筑能源规范（NECB）中参考暖通空调系统的优化选择。申请人建议发展一个大规模的多目标优化框架，以选择HVAC系统的配置和确定设备容量的大小。该框架将与建筑能源分析程序（例如，EnergyPlus, eQUEST）相连接。这是一个寻找最小生命周期能源使用、生命周期成本、生命周期能源破坏和生命周期环境影响这四个相互冲突的目标函数的问题。消除了无法保护居住者免受预期的室外威胁（物理，化学和生物）的HVAC系统的设计替代方案。提出的优化框架将用于为加拿大主要城市的不同建筑类型选择具有低能耗目标值的创新参考HVAC系统。本研究的结果有助于NECB的更新。