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

职业：全面评估互联建筑和人员对社区能源规划和管理的影响

• 批准号: 1845650
• 负责人: Bing Dong
• 金额: $ 50.02万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845650&HistoricalAwards=false
• 关键词: CAREER; Holistic; Assessment; Impacts; Connected

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.

未来的智能社区将包括连接的建筑物，分布式能源，电动汽车，电池储能系统以及在各个建筑物层面的点对点能源交易。 这些功能将需要建筑物集群，分布式能源和智能电网的最佳和分布式协调，同时考虑人类的行为和流动性。智能电表和社交媒体的开发和参与为社区能源计划开辟了新的范式，并强调了对整体工程框架的需求，以建模社区的新能源基础设施。 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 框架。该研究的目的是弥合数据科学与智能建筑技术之间的差距。为了应对所涉及的挑战，将追求以下研究任务：（1）开发一个新的数学框架来学习社区联系。将学习，分类和聚类，从异质数据集中获得乘员行为。将开发对乘员运动和存在的新马尔可夫建模； （2）开发一个创新的共同模拟平台，以将新的乘员行为模型与社区水平的能源建模相结合。将开发一个新的时空乘员模型，将存在和行为联系起来，并将整合社区城市能源基础设施的物理模型，并与新的行为模型相结合； （3）开发一个分层控制框架，以根据社区联系和集成的共模拟平台评估和量化从单个建筑物到整个社区的能源节省。具有预测控制体系结构的多层模型将旨在解决各种能源系统的时间分配问题。这种方法使它对一组不同的居住者行为有系统地了解如何影响运营决策和能源效率在未来的智慧城市中。如果成功，该方法可能会改变当前的可持续社区设计和运营，以积极涉及宏观居住者的行为和人类流动性建模。 PI将与UTSA现有的少数民族工程计划，学生组织和荣誉计划合作，旨在招募和研究生少数群体学生，例如NSF Louis Stokes for Mional参与（LSAMP），Ronald McNair Scholars计划，以及西班牙裔专业工程师协会。首席调查员还将维护一个网站，以使研究结果和在线游戏可供公众使用。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。