EAGER: User-Centric Interdependent Urban Systems: Using Multi-Modal Transportation Data for Demand Prediction and Management in Buildings

2015年，美国总能源消耗的40%来自建筑。建筑系统，如供暖、通风、照明、蒸汽和供水系统，是主要的最终用途。对公用事业需求的准确预测是有效管理和运营建筑系统以实现节能的核心。这个智能互联社区(S和CC)探索性研究早期概念基金(AGER)项目使用交通系统中的数据来预测建筑物的使用情况，并支持实时的建筑物系统管理。其基本原理是，人们按顺序使用交通工具和建筑物，并且可以根据用户出行的时间和数量来准确预测建筑物的占用率(在某种程度上还包括公用事业需求)，并提前几分钟或几小时前往建筑物。这一理论将在卡内基梅隆大学校园内选定的建筑中进行测试。这项拟议的研究如果成功，将以建筑系统和交通系统为例，为理解不同城市系统之间相互关系的复杂性质创造一个新的范式。它在节约能源和延长基础设施生命周期方面具有巨大的潜力，产生了显著的社会效益。它将为参与这项研究的学生提供跨学科的培训，涉及土木工程、数据科学、设施管理和控制理论。PIS将组织几个基于网络的研讨会，将理论和研究结果带给更广泛的学生、研究人员和城市系统管理人员。研究结果将通过期刊出版物、会议和研讨会进行传播。本研究的目标是融合和分析有关交通和建筑系统使用的高分辨率实时数据，并发现这些系统之间的用户模式的时空关联。时空相关性如果被发现，对于能够以有效和高效的方式进行跨系统需求预测和管理至关重要。特别是，本研究通过对所有这些系统的海量数据进行整体挖掘，更好地理解了道路、交通、停车和建筑系统之间的相互依赖关系。一个关键的组成部分是学习联合交通建设网络中典型的循环和非循环流动模式。此外，这项研究开发了一种数据驱动的方法，该方法自动检测建筑物占用级别与各种建筑系统所需负荷之间的关系，以开发基于占用负荷的控制的定制模型。它还开发了闭环系统控制机制，用于实时控制建筑系统的收缩。

In 2015, 40 percent of total energy consumption in the U.S. was attributed to buildings. Building systems, such as heating, ventilation, lighting, steam and water supply systems, were the dominant end uses. Accurate predictions of utility demand are at the heart of efficiently managing and operating building systems for energy saving. This Smart and Connected Communities (S&CC) EArly-concept Grant for Exploratory Research (EAGER) project uses data in transportation systems to predict building occupancy and support real-time building system management. The rationale is that people use transportation and buildings sequentially, and the building occupancy (and to some extent utility demand) can be accurately predicted according to when and how many users are traveling and heading to buildings minutes or hours ahead. This theory will be tested in selected buildings on Carnegie Mellon University campus. The proposed research, if successful, creates a new paradigm for understanding the complex nature of interrelationships among various urban systems, using building systems and transportation systems as examples. It has great potentials to save energy and expand infrastructure life cycles, resulting in significant societal benefits. It will provide interdisciplinary training to students involved in this research on civil engineering, data science, facility management and control theory. The PIs will organize several web-based seminars to bring the theories and findings to a broader audience of students, researchers and urban system managers. The research results will be disseminated through journal publications, conferences, and workshops.The objective of this research is to fuse and analyze high-resolution real-time data regarding the usages of transportation and building systems and discover spatio-temporal correlations of user patterns among those systems. The spatio-temporal correlations would be critical, if discovered, for enabling cross-system demand prediction and management in an effective and efficient manner. In particular, this research gains a better understanding of the interdependency of roadway, transit, parking and building systems through holistically mining the massive data of all those systems. A critical component is to learn typical recurrent and non-recurrent flow patterns in a joint transportation-building network. In addition, this research develops a data-driven approach that automatically detects relations between building occupancy levels and required loads for various building systems to develop customized models for occupancy-load-based control. It also develops closed-loop control mechanisms for setback of building systems in real time.