Optimal sensor placement in smart buildings

根据联合国的说法，建筑部门最有潜力实现具有成本效益的温室气体减排。此外，建筑物需要变得更具适应气候变化的能力，以及更能适应不断变化的商业需求。由于人工智能和传感技术最近的发展，现在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容易负担得起。然而，更广泛地采用数据驱动的分析有一个主要障碍，那就是在建筑物中，他们总是依赖传感器来捕获运行数据。目前，还没有确定在哪里安装传感器、选择哪些传感器以及测量什么的计算机化方法。现有的建筑物传感能力设计大多依赖于过时的启发式方法和模糊的经验法则。 这项建议的目的是开发自动确定建筑系统中传感器的最佳位置的方法，特别是以下三个领域： 1)建筑系统中虚拟计量和关键性能指标(KPI)的最低感测要求。尤其是虚拟计量，允许建筑物所有者使用一组更便宜的传感器来取代昂贵或无法测量的价值。 2)建筑暖通空调系统中传感器的优化选择和布置，以提高系统的可观测性和故障隔离性。目标是确定能够可靠地识别暖通空调系统中的关键故障所需的最佳感知量，并避免以投诉为导向的操作转向基于性能的操作。 3)放置照明和温度传感器，以灵活地使用空间。这是为了确保传感器安装在占用的空间内可以适应未来的布局变化，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200多座与现有合作者合作的商业建筑的数据。 上述建议的研究将由申请者和四名高素质人员(HQP)执行，其中包括两名博士生、两名硕士和两名本科生。学员将在机器学习等数据驱动的分析以及优化、动力系统和建筑物理等基本工程理论方面获得丰富的经验。这些技能受到政府机构、行业和教育机构的高度追捧。这项研究有望开发用于建筑物传感器优化布置的计算机化工具。这有助于降低在建筑物中采用数据驱动分析的门槛。正在进行翻新的现有建筑可以很容易地确定数据驱动分析所需的额外传感器，而新建筑可以在设计阶段计算这些位置。

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 Most of the existing design of sensing capabilities in buildings rely on outdated heuristics and vague rule of thumbs.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.