BIM and IoT-enabled Smart Continuous Commissioning

基于 BIM 和物联网的智能连续调试

• 批准号: RGPIN-2018-04105
• 负责人: McArthur, Jennifer
• 金额: $ 2.26万
• 依托单位: Ryerson 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=743822
• 关键词: BIM; IoT; enabled; Smart; Continuous

Buildings are a critical component of Canada's infrastructure and economy and play a major role in energy use (40% of total), GHG emissions (33% of total), and occupant health and productivity. The correction of operational inefficiencies (e.g. poor controls logic, faulty equipment, etc.) can provide an estimated 20-30% energy savings in each building with long-term economic and environmental benefits. Facility Management (FM) practices are beginning to leverage Smart Buildings technologies to leverage sensor networks and integrate multiple systems to address resilience, emergency preparedness, occupant health and productivity, and reduce energy and CO2 impacts. New tools, insights, and strategies are necessary to support this transition and enable Canadian buildings to achieve new levels of operational efficiency.The proposed research program leverages the PI's previous research in energy analytics, Facilities Management (FM) enabled Building Information Modeling (FM-BIM), and integrating machine learning into FM system. Through long-term collaboration with the FM team at Ryerson University, two buildings under construction (opening 2018) have been outfitted with sensor networks and will stream to the Virtual Campus Model previously created. A third building to be completed in 2019 will host the new Smart Building Analytics Lab opening in 2019) and provide a living lab to test and demonstrate new controls and operations strategies. The proposed research will start with the development of a data model expanding existing IoT and BIM ontologies to support Smart Continuous Commissioning. Data obtained from the building sensor networks, and institutional facility management systems will be mapped using this structure into a Building Information Model (BIM) to test the robustness of this model and permit spatio-temporal visualization. Physical and virtual testbeds will be used to prototype, develop, and test data retrieval, mining, analysis, and management at the IoT devices using both supervised and unsupervised machine learning approaches. The PI's new Smart Analytics Laboratory along with a campus FM-BIM (to both provide facility data to the IoT devices and visualize the near-real-time performance) and Matlab building emulator will serve as the physical and virtual testbeds for experimentation.Over the five-year duration of the grant, it is expected that this research will lead to novel data integration and management approaches based on the new data model, new fault detection, trending, and analysis techniques, and a Smart Continuous Commissioning (SCCx) system prototype. This research will reduce the barriers to continuous commissioning, facilitating the maintenance of peak operational performance within buildings with the potential to significantly decrease energy consumption and associated greenhouse gases.

建筑物是加拿大基础设施和经济的重要组成部分，在能源使用（占总量的40%），温室气体排放（占总量的33%）以及居住者健康和生产力方面发挥着重要作用。纠正运营效率低下的问题（如控制逻辑不佳、设备故障等）估计每幢楼宇可节省20-30%的能源，并带来长远的经济及环境效益。设施管理（FM）实践开始利用智能建筑技术来利用传感器网络并集成多个系统，以解决弹性，应急准备，居住者健康和生产力，并减少能源和二氧化碳的影响。新的工具，见解和策略是必要的，以支持这一转变，使加拿大建筑，以实现新的水平的运营效率。拟议的研究计划利用PI的能源分析，设施管理（FM）启用建筑信息建模（FM-BIM），并集成机器学习到FM系统以前的研究。通过与瑞尔森大学FM团队的长期合作，两座在建建筑（2018年开放）已经配备了传感器网络，并将流到之前创建的虚拟校园模型。第三座建筑将于2019年竣工，将于2019年开放新的智能建筑分析实验室），并提供一个生活实验室来测试和演示新的控制和运营策略。拟议的研究将从开发一个数据模型开始，扩展现有的物联网和BIM本体，以支持智能持续调试。从建筑传感器网络和机构设施管理系统获得的数据将使用该结构映射到建筑信息模型（BIM）中，以测试该模型的鲁棒性并允许时空可视化。物理和虚拟测试平台将用于使用监督和无监督机器学习方法在物联网设备上进行原型设计，开发和测试数据检索，挖掘，分析和管理。PI的新智能分析实验室沿着校园FM-BIM（既为物联网设备提供设施数据，又将接近实时的性能可视化）和Matlab建筑仿真器作为实验的物理和虚拟测试平台。在五年的资助期内，预计这项研究将导致基于新数据模型的新数据集成和管理方法，新的故障检测、趋势分析和分析技术，以及智能连续调试（SCCx）系统原型。这项研究将减少连续调试的障碍，促进建筑物内保持峰值运行性能，并有可能显著降低能耗和相关温室气体。