Optimal sensor placement in smart buildings

智能建筑中的最佳传感器放置

• 批准号: RGPIN-2020-06489
• 负责人: Shi, Zixiao
• 金额: $ 1.68万
• 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: 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=741247
• 关键词: Optimal; sensor; placement; smart; buildings

According to the United Nations, the building sector has the most potential to achieve cost-effective greenhouse gas emission reductions. Furthermore, buildings need to become more resilient to climate change, as well as more adaptable to the changing commercial requirements. Thanks to the recent development in artificial intelligence and sensing technologies, it is now more affordable to improve operation effectiveness in buildings through software updates instead of costly equipment replacements. Yet there is one major hurdle to the wider adoption of data--driven analytics in buildings - they invariably rely on sensors to capture operation data. Currently, there are no computerized methods for determining where to install sensors, which sensors to select and what to measure. Most of the existing design of sensing capabilities in buildings rely on outdated heuristics and vague rule of thumbs. The aim of this proposal is to develop ways to automatically determine the optimal placement of sensors in building systems, specifically the following three areas: 1) Minimal sensing requirements for virtual metering and key performance indicators (KPI) in building systems. Virtual metering in particular allows building owners use a collection of cheaper sensors to replace otherwise expensive or immeasurable values. 2)Optimal selection and placement of sensors in building HVAC systems to enhance observability and fault isolability. The goal is to determine the optimal amount of sensing required to be able to reliably identify critical faults in HVAC systems, and to steer away from complaint--driven operation to performance- based. 3)Placement of lighting and temperature sensor for flexible space usage. This is to ensure sensor installation within the occupied space can accommodate future layout changes, and provide optimal individual controllability. The main sources of data for this research come from the existing Living lab buildings at Carleton University, as well as data from more than 200 commercial buildings with existing collaborators. The above proposed research will be executed by the applicants and four highly qualified personnel (HQP), including two PhD students, two master's students and two undergraduate students. Trainees will gain significant experience in data--driven analytics such as machine learning, and fundamental engineering theories including optimization, dynamic systems and building physics. These skills are highly sought after by government institutions, industry and education institutes. The research is expected to develop computerized tools for optimal sensor placement in building sensors. This can help lower the barriers to adopting data--driven analytics in buildings. Existing buildings going through a retrofit can easily determine additional sensors needed for data--driven analytics, and new constructions can have these placements calculated during the design stage.

根据联合国的说法，该建筑行业具有实现成本效益的温室气体排放量最大的潜力。此外，建筑物需要变得更加有弹性，并更适合不断变化的商业需求。由于最近在人工智能和传感技术方面的发展，通过软件更新而不是昂贵的设备更换来提高建筑物的运行效率更高。然而，在建筑物中驱动的分析驱动分析的更广泛采用存在一个主要障碍 - 它们总是依靠传感器来捕获操作数据。当前，尚无计算机化方法来确定在哪里安装传感器，要选择哪些传感器以及测量什么。建筑物中传感能力的大部分设计都取决于过时的启发式和模糊的拇指规则。 该建议的目的是开发自动确定传感器在建筑系统中的最佳位置的方法，特别是以下三个方面：1）虚拟计量和关键性能指标（KPI）在建筑系统中的最小传感要求。尤其是虚拟计量，允许建筑所有者使用廉价传感器的集合来替换原本昂贵或不可估量的值。 2）在构建HVAC系统中的最佳选择和放置传感器，以增强可观察性和故障分离性。目的是确定能够可靠地识别HVAC系统中的关键故障所需的最佳感应量，并驱逐出投诉（驱动的操作）至基于绩效的操作。 3）放置照明和温度传感器以使用灵活的空间。这是为了确保在占用空间内的传感器安装可以适应未来的布局更改，并提供最佳的个人可控性。这项研究的主要数据来源来自卡尔顿大学现有的生活实验室建筑，以及来自现有合作者的200多个商业建筑的数据。上述研究将由申请人和四名高素质人员（HQP）执行，其中包括两名博士学位学生，两名硕士学生和两名本科生。受训者将在数据驱动的分析中获得丰富的经验，例如机器学习和基本工程理论，包括优化，动态系统和建筑物物理学。这些技能受到政府机构，行业和教育机构的高度追捧。预计该研究将开发计算机化工具，以在建筑传感器中放置最佳的传感器。这可以帮助降低采用数据驱动分析的障碍。经过改造的现有建筑物可以轻松确定数据所需的其他传感器 - 驱动分析，新的结构可以在设计阶段计算出这些位置。