CAREER: Holistic Assessment of the Impacts of Connected Buildings and People on Community Energy Planning and Management

未来的智能社区将由互联建筑、分布式能源、电动汽车、电池储能系统和个人建筑层面的点对点能源交易组成。这些功能将需要对建筑群、分布式能源和智能电网进行优化和分布式协调，同时考虑人类行为和移动性。智能电表和社交媒体的开发和参与开启了社区能源规划的新范式，并强调了需要一个整体工程框架来模拟社区的新能源基础设施。该项目旨在通过综合研究和教育计划，弥合智能建筑技术、社会互动、能源交易和电网运营之间的差距。在该项目中，将开发一种方法，通过使用大规模异质数据集，包括智能电表、居住者行为和社交媒体数据，城市规模的能源和行为联合模拟平台，以及先进的建筑、电网和分布式能源控制框架，来推进当前最先进的社区能源规划和管理技术。这项研究旨在弥合数据科学和智能建筑技术之间的差距。为了应对所涉及的挑战，将进行以下研究任务：(1)开发一个新的数学框架来学习社区联系。从不同的数据集中学习、分类和聚类乘客行为。将开发一种新的乘员运动和在场的马尔可夫模型；(2)开发一个创新的联合仿真平台，将新的乘员行为模型与社区级能量建模相结合。将开发一个连接在场和行为的新的时空居住者模型，并将社区城市能源基础设施的物理模型与新的行为模型相结合；以及(3)基于社区连接和集成的联合仿真平台，开发一个分级控制框架来评估和量化从单个建筑到整个社区的节能效果。具有预测控制结构的多层模型将被设计来解决各种能源系统的时间差异问题。这种方法着眼于系统地了解一组不同的乘员行为如何影响未来智能城市的运营决策和整体能效。如果成功，该方法可能会改变目前可持续的社区设计和运营，积极参与宏观层面的居住者行为和人类流动性建模。PI将与UTSA现有的少数族裔工程项目、学生组织和旨在招收和毕业少数族裔学生的荣誉项目合作，如NSF Louis Stokes少数群体参与联盟(LSAMP)、罗纳德·麦克奈尔学者项目和西班牙裔专业工程师协会。首席调查员还将维护一个网站，将研究成果和在线游戏提供给公众。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Future smart communities will consist of connected buildings, distributed energy resources, electric vehicles, battery energy storage systems, and peer-to-peer energy trading at the individual building level. These features will require an optimal and distributed coordination of a cluster of buildings, distributed energy resources, and a smart grid, while considering human behavior and mobility. The development and engagement of smart meters and social media have opened a new paradigm for community energy planning and underscore the need for a holistic engineering framework to model a new energy infrastructure of a community. This project aims to bridge this gap between smart building technologies, social interactions, energy trading and grid operation through an integrated research and education program.In the project, an approach will be developed that advances current state-of-the-art community energy planning and management technology through use of large-scale heterogeneous data sets, including smart meter, occupant behavior and social media data, an urban scale energy and behavior co-simulation platform, and an advanced integrated building, grid and distributed energy resources control framework. The research aims to will bridge the gap between data science and smart building technologies. To address the challenges involved, the following research tasks will be pursued: (1) Develop a new mathematical framework to learn community connections. Occupant behavior from heterogeneous data sets will be learned, classifies and clustered. A new Markov modeling of occupant movement and presence will be developed; (2) Develop an innovative co-simulation platform to integrate a new occupant behavior model with community level energy modeling. A new spatiotemporal occupant model linking presence and behavior will be developed, and physical models of community urban energy infrastructure will be integrated and coupled with a new behavior model; and (3) Develop a hierarchical control framework to evaluate and quantify energy savings from individual buildings to a whole community based on community connections and an integrated co-simulation platform. A multilayer model with predictive control architecture will be designed to address the time-discrepancy issuesof various energy systems. This approach sets it sights on a systematic understanding of how a set of diverse occupant behavior impacts operation decisions and energy efficiency as a whole in a future smart city. If successful, the approach may transform current sustainable community design and operation to actively involve macro-level occupant behavior and human mobility modeling. The PI will work with existing minority engineering programs, student organizations, and honors programs at UTSA designed to recruit and graduate minority students, such as the NSF Louis Stokes Alliances for Minority Participation (LSAMP), the Ronald McNair Scholars programs, and the Society of Hispanic Professional Engineers. The principal investigator will also maintain a website to make the research results and online games available to the public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.