Data-driven optimization of light commercial buildings' operation

数据驱动轻型商业建筑运营优化

• 批准号: 576761-2022
• 负责人: Ouf, MohamedMMMA
• 金额: $ 1.46万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=746365
• 关键词: Data; driven; optimization; light; commercial

The proposed project will establish a partnership between researchers at Concordia University and Strato Automation, a Montreal-based company focusing on building automation. It aims to develop a holistic workflow for optimizing the operation of light commercial buildings by integrating advanced control strategies. More specifically, it will focus on 1) creating new algorithms for automated fault detection and diagnostics (AFDD) that leverage data-mining to identify faulty operations, and 2) developing easily deployable model-based predictive control algorithms (MPC) to optimize energy use and peak demand, based on day-ahead weather predictions. The ultimate goal will be expanding the capabilities of the industry partner's Strato Light Commercial (SLC) system to optimize the operation of light commercial buildings. This will provide tangible benefits to the Canadian economy at large, given the unprecedented shift to remote working in the aftermath of COVID-19. This dramatic shift is likely here to stay, which is why optimizing the operation light commercial buildings is gaining significant interest. With access to hundreds of commercial buildings across North America, the partner organization will leverage the outcomes of this research to complement their existing libraries of solutions and algorithms for smart building controls using SLC. Ultimately, these solutions will strengthen their position as a global leader in Efficient and Data-Driven Building Automation. Research outcomes can also be easily deployed at minimal capital cost in many of Canada's over half a million commercial buildings, which collectively consume over 850 PJ of energy. Furthermore, this partnership will provide Highly Qualified Personnel (HQP) with a unique opportunity to gain relevant industry experience that will allow them to fully understand building systems and controls technologies. They will work with data from real buildings and interact with their systems and components, while gaining interdisciplinary research skills combining building engineering with data science; both of which are strongly and urgently needed in the Canadian building industry.

拟议中的项目将在康科迪亚大学的研究人员和总部位于蒙特利尔的专注于建筑自动化的Strato Automation公司之间建立合作伙伴关系。它旨在通过整合先进的控制策略，制定一套全面的工作流程，以优化轻型商业建筑的运营。更具体地说，它将专注于1)创建利用数据挖掘来识别故障操作的自动故障检测和诊断(AFDD)的新算法，以及2)开发易于部署的基于模型的预测控制算法(MPC)，以根据提前一天的天气预报优化能源使用和高峰需求。最终目标将是扩大行业合作伙伴的Strato Light Commercial(SLC)系统的能力，以优化轻型商业建筑的运营。这将为加拿大整体经济带来实实在在的好处，因为新冠肺炎带来了前所未有的向远程工作的转变。这种戏剧性的转变可能会持续下去，这就是为什么优化轻型商业建筑的运营受到了极大的关注。通过访问北美的数百座商业建筑，合作伙伴组织将利用这项研究的成果来补充其现有的解决方案库和使用SLC的智能建筑控制算法。最终，这些解决方案将巩固其作为高效和数据驱动的建筑自动化领域全球领导者的地位。研究成果还可以以最低的资本成本轻松部署在加拿大超过50万座商业建筑中的许多建筑中，这些建筑总共消耗超过850磅焦耳的能源。此外，这一合作关系将为高素质人员(HQP)提供获得相关行业经验的独特机会，使他们能够充分了解建筑系统和控制技术。他们将使用真实建筑的数据，并与其系统和组件进行交互，同时获得将建筑工程与数据科学相结合的跨学科研究技能；这两者都是加拿大建筑业强烈而迫切需要的。