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: 1949372
• 负责人: Bing Dong
• 金额: $ 50.02万
• 依托单位: Syracuse University
• 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: 2019-08-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949372&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互联建筑、分布式能源、电动汽车、电池储能系统以及单个建筑层面的点对点能源交易。 这些功能将需要建筑群、分布式能源和智能电网的优化和分布式协调，同时考虑人类行为和移动性。智能电表和社交媒体的发展和参与为社区能源规划开辟了一个新的范例，并强调需要一个整体的工程框架来模拟社区的新能源基础设施。该项目旨在通过一个综合的研究和教育计划，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，通过使用大规模异构数据集，包括智能电表、居住者行为和社交媒体数据，一个城市规模的能源和行为协同仿真平台，以及一个先进的集成建筑，电网和分布式能源控制框架。该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。来自异构数据集的乘客行为将被学习、分类和聚类。将开发一种新的居住者运动和存在的马尔可夫模型;（2）开发一个创新的协同仿真平台，将新的居住者行为模型与社区级能量建模相结合。将开发一个新的时空居住者模型，将存在和行为联系起来，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;（3）开发一个分层控制框架，以评估和量化从单个建筑到整个社区的节能，基于社区连接和集成的协同仿真平台。一个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居住者行为如何影响未来智慧城市的整体运营决策和能源效率。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，积极参与宏观层面的居住者行为和人类流动建模。PI将与UTSA现有的少数民族工程项目，学生组织和荣誉项目合作，旨在招募和毕业少数民族学生，如NSF路易斯斯托克斯少数民族参与联盟（LSAMP），罗纳德麦克奈尔学者计划和西班牙裔专业工程师协会。主要研究者还将维护一个网站，向公众提供研究成果和在线游戏。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods

微观和宏观层面的极端事件、能源安全和平等：概念、挑战和方法

• DOI: 10.1016/j.erss.2021.102401
• 发表时间: 2022
• 期刊: Energy Research & Social Science
• 影响因子: 6.7
• 作者: Chen, Chien-fei;Dietz, Thomas;Fefferman, Nina H.;Greig, Jamie;Cetin, Kristen;Robinson, Caitlin;Arpan, Laura;Schweiker, Marcel;Dong, Bing;Wu, Wenbo
• 通讯作者: Wu, Wenbo

Extending the Brick schema to represent metadata of occupants

扩展 Brick 模式以表示占用者的元数据

• DOI: 10.1016/j.autcon.2022.104307
• 发表时间: 2022
• 期刊: Automation in Construction
• 影响因子: 10.3
• 作者: Luo, Na;Fierro, Gabe;Liu, Yapan;Dong, Bing;Hong, Tianzhen
• 通讯作者: Hong, Tianzhen

A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption

• DOI: 10.1016/j.apenergy.2020.115656
• 发表时间: 2020-11
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: Wenbo Wu;B. Dong;Qi Wang;M. Kong;D. Yan;Jingjing An;Yapan Liu

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

• DOI: 10.1016/j.buildenv.2020.106964
• 发表时间: 2020-06
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: F. Salim;B. Dong;M. Ouf;Qi Wang;I. Pigliautile;Xuyuan Kang;T. Hong;Wenbo Wu;Yapan Liu

Documenting occupant models for building performance simulation: a state-of-the-art

• DOI: 10.1080/19401493.2022.2061050
• 发表时间: 2022-09
• 期刊: Journal of Building Performance Simulation
• 影响因子: 2.5
• 作者: M. Vellei;Elie Azar;Karol Bandurski;C. Berger;S. Carlucci;Bing Dong;Matteo Favero;A. Mahdavi