III: Small: Cyber Physical Mappings - Empower Building Analytics at Scale

III：小型：网络物理映射 - 增强大规模建筑分析能力

• 批准号: 1718216
• 负责人: Hongning Wang
• 金额: $ 49.99万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718216&HistoricalAwards=false
• 关键词: III; Small; Cyber; Physical; Mappings

Buildings have profound impact on human health, productivity, comfort, and energy consumption. For example, building operation is the single largest energy consumer in the US, accounting for 70% of electricity consumption and 40% of total energy consumption. Allergens, noise levels, and the availability of sunlight affect health and well-being, especially given that on average Americans spend 90% of their time in buildings. Indoor conditions such as thermal comfort and CO2 and pollutant concentrations have been shown to affect human productivity by 8-11%, which has an important effect on the national economy. The performance of the nation's buildings can be significantly improved with analytics engines that collect and analyze data from the thousands of sensing and control points that already exist within a typical building. However, data alone does not inherently have any meaning, and so a person must manually provide the context (also called metadata) about every sensor and controller so that the analytics engine can interpret the data. This costly manual process can take days or weeks for a single building and is a major obstacle for applying building analytics to a large number of buildings.This project creates tools to automatically infer the metadata of data streams, such as the type of sensor or controller that produced the data and its relation to other sensors, equipment, or rooms in the building. The approach is based on the hypothesis that the data in buildings is structured due to weather patterns, equipment operation patterns, and common design patterns that are observed in many buildings around the world. Metadata inference exploits this structure to quickly and easily create new metadata values for a large number of sensing and control points based on known metadata of other points or other buildings. It develops new learning-based techniques along three main research thrusts: 1) value inference of individual points, 2) relationship inference between sensors, and 3) latent metadata inference from building managers' interactive access behaviors with a building management system. This research enables industry and institutions to more easily apply building analytics to new buildings with minimal or even no manual mapping required. It generates impact on average US building performance along multiple metrics, including human health, productivity, comfort, and energy consumption. In addition, the proposed research includes the development of fundamentally new methods and techniques in the fields of data mining and cyber-physical systems, and they will be released as open-sourced code. The research activities will be incorporated into teaching materials for student training and education. Both graduate and undergraduate researchers will be involved in all phases of this research, and we will engage and recruit students from underrepresented groups to participate in this research. If successful, these techniques will generalize to other types of C activities such as human health monitoring, infrastructure monitoring, or smart transportation systems where structure can similarly be used to help infer the physical context of a sensor or controller.

建筑物对人类健康、生产力、舒适度和能源消耗有着深远的影响。例如，建筑运营是美国最大的单一能源消费者，占电力消耗的70%和总能源消耗的40%。过敏原、噪音水平和阳光的可用性都会影响健康和福祉，特别是考虑到美国人平均90%的时间都在建筑物中度过。室内热舒适度、CO2和污染物浓度等因素对人类生产力的影响高达8- 11%，对国民经济有重要影响。分析引擎可以从典型建筑物内已经存在的数千个传感和控制点收集和分析数据，从而显著提高国家建筑物的性能。然而，数据本身本身并不具有任何意义，因此人们必须手动提供关于每个传感器和控制器的上下文（也称为元数据），以便分析引擎可以解释数据。对于单个建筑物来说，这种昂贵的手动过程可能需要数天或数周时间，这是将建筑分析应用于大量建筑物的主要障碍。该项目创建了自动推断数据流元数据的工具，例如产生数据的传感器或控制器的类型及其与建筑物中其他传感器、设备或房间的关系。该方法是基于这样的假设，即建筑物中的数据是结构化的，这是由于天气模式，设备操作模式和常见的设计模式，在世界各地的许多建筑物中观察到的。元数据推断利用这种结构，基于其他点或其他建筑物的已知元数据，快速轻松地为大量传感和控制点创建新的元数据值。它沿着沿着三个主要研究方向开发新的基于学习的技术：1）个体点的值推断，2）传感器之间的关系推断，以及3）来自建筑管理者与建筑管理系统的交互访问行为的潜在元数据推断。这项研究使行业和机构能够更轻松地将建筑分析应用于新建筑，只需很少甚至不需要手动映射。它对美国建筑的平均性能产生了沿着多个指标的影响，包括人类健康、生产力、舒适度和能源消耗。此外，拟议的研究还包括在数据挖掘和网络物理系统领域开发全新的方法和技术，并将以开源代码的形式发布。研究活动将纳入学生培训和教育的教材。研究生和本科生研究人员都将参与这项研究的所有阶段，我们将从代表性不足的群体中招募学生参与这项研究。如果成功，这些技术将推广到其他类型的C活动，如人类健康监测，基础设施监测或智能交通系统，其中结构可以类似地用于帮助推断传感器或控制器的物理环境。

项目成果

Graph-based Extractive Explainer for Recommendations

• DOI: 10.1145/3485447.3512168
• 发表时间: 2022-02
• 期刊: Proceedings of the ACM Web Conference 2022
• 作者: Peifeng Wang;Renqin Cai;Hongning Wang

Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback

• DOI: 10.1145/3477495.3532057
• 发表时间: 2022-06
• 期刊: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Yiling Jia;Hongning Wang

Category-aware Collaborative Sequential Recommendation

• DOI: 10.1145/3404835.3462832
• 发表时间: 2021-07
• 期刊: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Renqin Cai;Jibang Wu;Aidan San;Chong Wang;Hongning Wang

Learning from Correlated Events for Equipment Relation Inference in Buildings

从相关事件中学习以进行建筑物中的设备关系推理

• DOI: 10.1145/3360322.3360852
• 发表时间: 2019
• 期刊: and Transportation
• 作者: Hong, Dezhi;Cai, Renqin;Wang, Hongning;Whitehouse, Kamin
• 通讯作者: Whitehouse, Kamin

Learning Personalized Topical Compositions with Item Response Theory

• DOI: 10.1145/3289600.3291022
• 发表时间: 2019-01
• 期刊: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
• 作者: Lu Lin;Lin Gong;Hongning Wang