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、Constantine E。该项目将使用数据驱动的方法，通过城市能源需求、行为和基础设施的耦合模型，增进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限度地发挥影响，以更有效地设计、实施和评估节能战略。该研究利用 PI 获取的多样化、全面且独特的数据集，旨在解决以下问题：(1) 是什么推动了城市内部和跨城市的能源使用？ (2) 建筑能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何因建筑类型、城市和地区而异？ (4) 城市能源利用的时空格局如何？ (5) 随着时间的推移，城市和区域政策如何影响能源效率和成本节约？城市信息学和大都市能源动态方面的工作重点是开发新的分析方法，并结合来自美国的一系列建筑、土地使用和能源数据。和国际城市，以促进对城市能源需求和建筑环境温室气体排放的模式和决定因素及其对人类福祉的影响的基本了解。这将通过整合土木和系统工程、数据科学和计算社会科学的方法来实现，以开发数据驱动的模型，通过从大数据中提取可操作的情报来支持决策。这项研究将 (1) 整合数以万计的建筑物和多个城市的一系列建筑物、社区和城市级别的数据，(2) 利用新的城市能源数据来源创建一个大型的、非自选的数据集， (3) 同时检查城市动态的物理、环境、社会和行为组成部分，以创建大都市区能源需求和减排潜力的多标量模型。该研究旨在提供严谨的分析来支持客观的、基于证据的政策，从而为拟议和实施的战略的绩效驱动评估创建一个框架。该教育计划将促进在建筑能源效率、城市信息学和城市可持续发展方面接受培训的学生网络的发展，并提高公众对解决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（由 34 个城市大学合作伙伴组成的团体，专注于应对城市挑战的数据解决方案）的现有关系基础上，为智能、可持续城市奠定基础。美国和全球。

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）

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：
2018-01
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

The impact of mandatory energy audits on building energy use

强制能源审计对建筑能源使用的影响