BIGDATA: Collaborative Research: IA: Big Data Analytics for Optimized Planning of Smart, Sustainable, and Connected Communities

BIGDATA：协作研究：IA：用于智能、可持续和互联社区优化规划的大数据分析

• 批准号: 1802017
• 负责人: Wangda Zuo
• 金额: $ 42.74万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-21 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802017&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; IA; Big

Transforming villages, towns, and cities into smart, connected, and sustainable communities is one of the most critical technological challenges of the coming decade. Realizing this vision is contingent upon enabling existing community infrastructure such as transportation, communications, and energy systems, to seamlessly integrate sustainable components such as renewable sources, smart sensors, and electric vehicles. Such an integration will ensure that tomorrow's communities are truly sustainable and connected by exhibiting desirable qualities that include: a) zero energy, in that they are self-sufficient in their energy production, b) zero outage, in that communication links across the community are ultra-reliable and experience significantly low interruption, and c) zero congestion, in that the traffic congestion is minimized across the community. With this overarching vision, the goal of this project is to develop a new planning framework for smart, connected and sustainable communities that allows meeting such zero-energy, zero-outage, and zero-congestions goals by optimally deciding on how, when, and where to deploy or upgrade a community's infrastructure. These decisions will be driven by massive volumes of community data, stemming from multiple sources that can include mobility, energy, traffic, communication demands, and other socio-technological information, to make informed decisions on how to gradually and organically transform a community into a fully sustainable and truly connected environment. The scale and heterogeneity of this problem necessitates the need for innovation in the tools used to process, analyze, and visualize heterogeneous data, as well as the data-aware metrics used to monitor the performance of this community infrastructure. One key element of this research is creation of a virtual testbed that can accurately reconstruct, simulate, and evaluate the theoretical framework by leveraging real-world big data sets from Virginia Tech and a zero-energy community in Florida as well as other sources, such as the DOE. The testbed is intended to be open access and will be able to support both research at host institution as well as other users requiring non-proprietary multi-domain open-data sets. The holistic nature of this research is thus expected to catalyze the global deployment of sustainable and connected communities. The proposed research will be complemented by a smart community big data challenge event that will enable broad community participation. The educational plan includes new big data-centric courses, as well as a large-scale involvement of graduate and undergraduate students in big data and smart communities research. Broad dissemination is ensured via open-source software and periodic workshops and tutorials. K-12 outreach events will be organized to attract under-represented student groups to big data research.This transformative research will lay the theoretical and practical foundations of smart, connected, and sustainable communities by developing the first big data-driven holistic approach to joint planning, optimization, and deployment of community infrastructure for systems of critical importance, such as communication, energy, and transportation networks. By bringing together interdisciplinary domain experts from data science, electrical engineering, and civil and architectural engineering, this research will yield several innovations: 1) Novel big data techniques for faithfully creating spatio-temporal models for smart communities that integrate data from heterogeneous sources and shed light on the composition and operation of a given smart community, 2) Novel, data-driven performance metrics that advance powerful mathematical tools from stochastic geometry to explicitly quantify the health of smart communities via tractable notions of zero energy, zero outage, and zero congestion, 3) Advanced analytical tools that bring forward novel ideas from optimization theory to devise the most effective strategies for deploying, upgrading, and operating various community infrastructure nodes, given the scale, dynamics, and structure of both the data and the community, and 4) A virtual smart community testbed that can accurately reconstruct, simulate, and evaluate the theoretical framework by leveraging open non-proprietary real-world big data sets.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.

将村庄、城镇和城市转变为智能、互联和可持续的社区是未来十年最关键的技术挑战之一。实现这一愿景取决于使现有的社区基础设施，如交通，通信和能源系统，无缝集成可再生能源，智能传感器和电动汽车等可持续组件。这样的整合将确保未来的社区是真正可持续的，并且通过展示期望的品质而连接，所述期望的品质包括：a）零能量，因为它们在其能量生产中是自给自足的，B）零中断，因为跨社区的通信链路是超可靠的并且经历显著低的中断，以及c）零拥塞，因为跨社区的交通拥塞被最小化。基于这一总体愿景，该项目的目标是为智能、互联和可持续的社区开发一个新的规划框架，通过优化决定如何、何时以及在何处部署或升级社区基础设施，实现零能耗、零中断和零能耗目标。这些决策将由大量社区数据驱动，这些数据来自多种来源，包括移动性，能源，交通，通信需求和其他社会技术信息，以便就如何逐步和有机地将社区转变为完全可持续和真正连接的环境做出明智的决策。 这个问题的规模和异构性需要在用于处理，分析和可视化异构数据的工具中进行创新，以及用于监视此社区基础设施性能的数据感知指标。这项研究的一个关键因素是创建一个虚拟测试平台，该平台可以通过利用弗吉尼亚理工大学和佛罗里达的零能耗社区以及其他来源（如美国能源部）的真实大数据集来准确地重建，模拟和评估理论框架。 该测试平台旨在开放获取，并将能够支持主办机构的研究以及其他需要非专有多域开放数据集的用户。 因此，这项研究的整体性有望促进可持续和互联社区的全球部署。拟议的研究将得到智能社区大数据挑战活动的补充，该活动将促进广泛的社区参与。该教育计划包括新的以大数据为中心的课程，以及研究生和本科生大规模参与大数据和智能社区研究。通过开源软件以及定期研讨会和教程确保广泛传播。通过组织K-12外联活动，吸引未被充分代表的学生群体参与大数据研究，这一变革性研究将通过开发首个大数据驱动的整体方法，为通信、能源和交通网络等至关重要的系统共同规划、优化和部署社区基础设施，奠定智能、互联和可持续社区的理论和实践基础。通过汇集来自数据科学，电气工程以及土木和建筑工程的跨学科领域专家，这项研究将产生几项创新：1）用于忠实地创建智能社区的时空模型的新型大数据技术，该智能社区集成来自异构源的数据并阐明给定智能社区的组成和操作，2）新颖，数据驱动的性能指标，从随机几何推进强大的数学工具，通过零能耗、零中断和零拥塞的易处理概念明确量化智能社区的健康状况，3）先进的分析工具，从优化理论提出新颖的想法，设计最有效的部署策略，升级和操作各种社区基础设施节点，给定数据和社区的规模，动态和结构，以及4）虚拟智能社区试验床，可以准确地重建，模拟，并通过利用开放的非专有的真实的-世界大数据集。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Evaluating the energy impact potential of energy efficiency measures for retrofit applications: A case study with U.S. medium office buildings

• DOI: 10.1007/s12273-021-0765-z
• 发表时间: 2021-03
• 期刊: Building Simulation
• 影响因子: 5.5
• 作者: Y. Ye;K. Hinkelman;Yingli Lou;W. Zuo;Gang Wang;Jian Zhang

Low-Cost Acoustic Sensor Array for Building Geometry Mapping using Echolocation for Real-Time Building Model Creation

• 发表时间: 2017
• 作者: T. A. Sevilla;W. Tian;Y. Fu;W. Zuo

Coupling fast fluid dynamics and multizone airflow models in Modelica Buildings library to simulate the dynamics of HVAC systems

• DOI: 10.1016/j.buildenv.2017.06.013
• 发表时间: 2017-09
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: W. Tian;T. A. Sevilla;W. Zuo;M. Sohn

Energy Prediction Impact of the Space Level Occupancy Schedule for a Primary School

小学空间占用表的能源预测影响

• 发表时间: 2021
• 期刊: Proceeding of the 17th Conference of International Building Performance Simulation Association (Building Simulation 2021
• 作者: Lou, Yingli;Ye, Yunyang;Zuo, Wangda;Zhang, Jan
• 通讯作者: Zhang, Jan

Modeling air-to-air plate-fin heat exchanger without dehumidification

• DOI: 10.1016/j.applthermaleng.2018.07.064
• 发表时间: 2018-10-01
• 期刊: APPLIED THERMAL ENGINEERING
• 影响因子: 6.4
• 作者: Zhou, G.;Ye, Y.;Zhou, X.
• 通讯作者: Zhou, X.