CAREER: Urban Informatics for Smart, Sustainable Cities: Toward a Data-Driven Understanding of Metropolitan Energy Dynamics

职业：智慧、可持续城市的城市信息学：以数据驱动的方式理解大都市能源动态

基本信息

批准号：
1653772
负责人：
Constantine Kontokosta
金额：
$ 51.03万
依托单位：
New York University
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：
2017-02-01 至 2022-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1653772&HistoricalAwards=false
关键词：
CAREER Urban Informatics Smart Sustainable

项目摘要

CBET 1653772; PI: Kontokosta, Constantine E. This project will use data-driven methodologies to advance fundamental understanding of urban energy dynamics through a coupled model of urban energy demand, behavior, and infrastructure. The work seeks to maximize impact by supporting evidenced-based urban energy policy, public and private decision-making, and infrastructure investment to more effectively design, implement, and evaluate energy reduction strategies. Using a diverse, comprehensive, and unique collection of data acquired by the PI, the research aims to tackle the following: (1) What drives energy use within and across cities? (2) What are the socio-technical dynamics of building energy consumption? (3) How and why do energy conservation measures and retrofit opportunities vary by building type, city, and region? (4) What are the spatial-temporal patterns of energy use in cities? (5) How do urban and regional policies impact energy efficiency and cost savings over time?The work in urban informatics and metropolitan energy dynamics is focused on developing new analytical approaches, coupled with an array of building, land use, and energy data from U.S. and international cities, to advance the fundamental understanding of the patterns and determinants of urban energy demand and GHG emissions from the built environment and their impacts on human well-being. This will be achieved by integrating methods from civil and systems engineering, data science, and computational social science to develop data-driven models to support decision-making through the extraction of actionable intelligence from big data. This research will (1) integrate an array of building, neighborhood, and city level data across tens of thousands of buildings and multiple cities, (2) utilize new sources of urban energy data to create a large, non-self-selected dataset, and (3) simultaneously examine physical, environmental, social, and behavioral components of urban dynamics to create a multi-scalar model of energy demand and reduction potentials across metropolitan areas. The research seeks to provide the analytical rigor to support objective, evidenced-based policies that will create a framework for performance-driven evaluation of proposed and implemented strategies. The education plan will facilitate development of a network of students trained in building energy efficiency, urban informatics, and urban sustainability, as well as foster greater public awareness of the scale and importance of addressing energy challenges in cities. The research and education activities will build on an existing relationship with city agencies, industry collaborators, and the MetroLab Network - a group of 34 city-university partnerships focused on data solutions to urban challenges - to provide the foundation for smart, sustainable cities in the U.S. and globally.

CBET 1653772; PI：Kontokosta，ConstantineE。该项目将使用数据驱动的方法来通过城市能源需求，行为和基础设施的耦合模型来提高对城市能源动态的基本了解。这项工作旨在通过支持基于证据的城市能源政策，公共和私人决策以及基础设施投资来最大程度地发挥影响力，以更有效地设计，实施和评估减少能源的策略。该研究使用PI获取的多样，全面和独特的数据集合，旨在解决以下内容：（1）是什么驱动城市内部和整个城市的能源使用？（2）建筑能源消耗的社会技术动态是什么？（3）节能措施和改造机会如何以及为什么会因建筑类型，城市和地区而异？（4）城市的能源使用的时空模式是什么？（5）城市和区域政策如何影响能源效率和随着时间的推移而节省成本？城市信息学和大都市能源动态的工作集中在开发新的分析方法上，再加上一系列的建筑物，土地使用以及来自美国和国际城市的能源数据，以促进对乌尔班和Ghg Emisss的基本上的基础，以促进对乌尔班的构建和确定性的基础。这将通过整合来自民用和系统工程，数据科学和计算社会科学的方法来开发数据驱动的模型，以通过从大数据中提取可行的情报来支持决策。 This research will (1) integrate an array of building, neighborhood, and city level data across tens of thousands of buildings and multiple cities, (2) utilize new sources of urban energy data to create a large, non-self-selected dataset, and (3) simultaneously examine physical, environmental, social, and behavioral components of urban dynamics to create a multi-scalar model of energy demand and reduction potentials across metropolitan areas.该研究旨在提供分析性的严格性，以支持基于目标的，有证据的政策，这些政策将为绩效驱动型和实施策略创建框架。该教育计划将促进建立一个培训的学生网络，该网络旨在建立能源效率，城市信息学和城市可持续性，并提高公众对应对城市能源挑战的规模和重要性的认识。研究和教育活动将建立在与城市机构，行业合作者和Metrolab网络的现有关系的基础上，这是一组由34个城市大学合作伙伴组成的集团，旨在针对城市挑战的数据解决方案 - 为美国和全球的智能，可持续的城市提供基础。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Mandatory building energy audits alone are insufficient to meet climate goals

仅强制建筑能源审计不足以实现气候目标

DOI：
10.1038/s41560-020-0603-z
发表时间：
2020
期刊：
Nature Energy
影响因子：
56.7
作者：
Kontokosta, Constantine E.;Spiegel-Feld, Danielle;Papadopoulos, Sokratis
通讯作者：
Papadopoulos, Sokratis

The impact of mandatory energy audits on building energy use

DOI：
10.1038/s41560-020-0589-6
发表时间：
2020-03-30
期刊：
NATURE ENERGY
影响因子：
56.7
作者：
Kontokosta, Constantine E.;Spiegel-Feld, Danielle;Papadopoulos, Sokratis
通讯作者：
Papadopoulos, Sokratis

A Dynamic Spatial-Temporal Model of Urban Carbon Emissions for Data-Driven Climate Action by Cities

城市碳排放动态时空模型，促进城市数据驱动的气候行动

DOI：
发表时间：
2018
期刊：
Bloomberg Data for Good Exchange 2018
影响因子：
0
作者：
Kontokosta, Constantine;Lai, Yuan;Bonzak, Bartosz;Papadopoulos, Sokratis;Hong, Boyeong;Johnson, Nicholas;Malik, Awais
通讯作者：
Malik, Awais

Large-scale parameterization of 3D building morphology in complex urban landscapes using aerial LiDAR and city administrative data