Award Abstract # 1642315
EAGER: Exploring the Coupled Dynamics of Urban Systems Using Data Science and Micro-Experimentation

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date: July 28, 2016
Latest Amendment Date: August 9, 2019
Award Number: 1642315
Award Instrument: Standard Grant
Program Manager: Walter Peacock
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: October 1, 2016
End Date: September 30, 2020 (Estimated)
Total Intended Award Amount: $146,722.00
Total Awarded Amount to Date: $201,722.00
Funds Obligated to Date: FY 2016 = $146,722.00
FY 2019 = $55,000.00
History of Investigator:
  • Rishee Jain (Principal Investigator)
    risheej@stanford.edu
Recipient Sponsored Research Office: Stanford University
450 JANE STANFORD WAY
STANFORD
CA  US  94305-2004
(650)723-2300
Sponsor Congressional District: 16
Primary Place of Performance: Stanford University
473 Via Ortega
Stanford
CA  US  94305-4027
Primary Place of Performance
Congressional District:
Unique Entity Identifier (UEI): HJD6G4D6TJY5
Parent UEI:
NSF Program(s): S&CC: Smart & Connected Commun,
GOALI-Grnt Opp Acad Lia wIndus
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 019Z, 029E, 036E, 1057, 7916, CVIS
Program Element Code(s): 033Y00, 150400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

This EArly-concept Grant for Exploratory Research (EAGER) project will help develop the underlying scientific and engineering foundation necessary to spawn the new technologies and systems necessary to make cities and communities more sustainable. Three to four blocks in downtown Palo Alto, California, will be instrumented to form a living lab for the collection of high-resolution data on building, energy and transportation infrastructure systems and the underlying human systems of the community. By engaging the City of Palo Alto in a close partnership, this project will have broad impacts in both the academic and civic communities. Results will be readily accessible and disseminated to Palo Alto municipal officials to empower municipal officials to make data-informed design, management and policy decisions. This work will include making study data and findings available to public as part of Palo Alto's Open Data Initiative thereby helping to promote a more engaged population and overall citizen well-being. This project will also help train an advanced scientific workforce capable of designing and managing our future cities and communities through pedagogical integration of the "living lab" into MS and PhD course offerings at Stanford University and a Massive Online Open Course (MOOC) initiative.

This project addresses the enormous pressure rapid urbanization is exerting on the myriad of complex and interdependent urban systems (e.g., energy, transportation, environmental, buildings). Changes in one system can have substantial impacts on others making it difficult to discern and predict the effects of urban design, management and policy decisions. This work aims to develop and employ a radically new data-driven micro-experimentation framework to characterize and quantify the coupled interactions and dynamics between urban infrastructure and human systems. Three to four blocks in downtown Palo Alto, California, will be outfitted with sensors to form a living lab for the collection of high-resolution data on building, energy and transportation infrastructure systems and the underlying human systems of the community. A multi-dimensional network model will be developed to simultaneously analyze multiple incoming urban data streams. Utilizing the results of the network data analysis, empirical micro-experiments will be conducted in this living lab to understand how changes in one urban system impact other systems. Ultimately, this project will contribute a novel micro-experimental framework for studying the coupled dynamics of urban systems that represents a radical shift away from viewing urban systems as purely technical, and integrates concepts from social policy to study urban systems from a socio-technical perspective.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 12)
Gupta, Karan and Yang, Zheng and Jain, Rishee K. "Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems" Journal of Computing in Civil Engineering , v.33 , 2019 https://doi.org/10.1061/(ASCE)CP.1943-5487.0000806 Citation Details
Jain, Rishee K. and Abraham, Dulcy "Computational Approaches to Enable Smart and Sustainable Urban Systems" Journal of Computing in Civil Engineering , v.33 , 2019 10.1061/(ASCE)CP.1943-5487.0000850 Citation Details
Nutkiewicz, Alex and Yang, Zheng and Jain, Rishee K. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow" Applied Energy , v.225 , 2018 10.1016/j.apenergy.2018.05.023 Citation Details
Nutkiewicz, Alex and Yang, Zheng and Jain, Rishee K. "Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow" Energy Procedia , v.142 , 2017 10.1016/j.egypro.2017.12.614 Citation Details
Roth, Jonathan and Bailey, Aimee and Choudhary, Sonika and Jain, Rishee K. "Resilient and Sustainable Urban and Energy Systems" ASCE International Conference on Computing in Civil Engineering 2019 , 2019 10.1061/9780784482445.059 Citation Details
Roth, Jonathan and Lim, Benjamin and Jain, Rishee K. and Grueneich, Dian "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective" Energy Policy , v.139 , 2020 https://doi.org/10.1016/j.enpol.2020.111327 Citation Details
Roth, Jonathan and Martin, Amory and Miller, Clayton and Jain, Rishee K. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods" Applied Energy , v.280 , 2020 https://doi.org/10.1016/j.apenergy.2020.115981 Citation Details
Sonta, Andrew J. and Jain, Rishee K. "Human-Technology Frontier, Sensing, and Computing" ASCE International Conference on Computing in Civil Engineering 2019 , 2019 10.1061/9780784482438.058 Citation Details
Srivastava, Charu and Yang, Zheng and Jain, Rishee K. "Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey" Building and Environment , v.157 , 2019 10.1016/j.buildenv.2019.04.016 Citation Details
Yang, Zheng and Gupta, Karan and Gupta, Archana and Jain, Rishee K. "A Data Integration Framework for Urban Systems Analysis Based on Geo-Relationship Learning" ASCE International Workshop on Computing in Civil Engineering 2017 , 2017 10.1061/9780784480823.056 Citation Details
Yang, Zheng and Gupta, Karan and Jain, Rishee K. "DUE-A: Data-driven Urban Energy Analytics for understanding relationships between building energy use and urban systems" Energy Procedia , v.158 , 2019 10.1016/j.egypro.2019.01.114 Citation Details
(Showing: 1 - 10 of 12)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Rapid urbanization is exerting enormous pressure on the myriad of complex and interdependent urban systems (e.g., energy, transportation, environmental, buildings). Changes in one system can have substantial impacts on others making it difficult to discern and predict the effects of urban design, management and policy decisions. This EArly-concept Grant for Exploratory Research (EAGER) project developed the underlying scientific and engineering foundation necessary to explore these complex and interdependent systems. Specifically, we explored the interdependencies between buildings, energy and transportation systems through our partnerships with the City of Palo Alto and Silicon Valley Clean Energy. As a result, this project yielded several novel computational frameworks to model, characterize and provide insights to inform policy-making to manage such interdependencies:

1.     Urban Data Integration (UDI) framework - framework to integrate multiple heterogeneous urban data streams through proximity relationship learning; enables computationally efficient querying and exploration of urban data spanning multiple systems (e.g., buildings, energy green space, transport).

2.     Data-driven Urban Energy Simulation (DUE-S) framework - framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow; enables urban scale modeling of building energy dynamics that is a precursor to analyzing interdependencies with other systems.

3.     Context-aware Urban Energy Analytics (CUE-A) framework - framework to empirically extract and quantify the relationships between building energy use and the spatial proximity of multiple surrounding urban systems; enables analysis of interactions between energy, building and transport systems to inform energy-aware urban design and land-use planning.

4.     SynCity: framework for creating synthetic city of hourly building energy estimates - a critical challenge in the analysis of interdependent urban systems is the availability of high-fidelity data streams, this framework overcomes this challenge by combining open data sets with physics-based simulation models to produce synthetic hourly load curve estimates for every building within a city; enables synergistic planning of interdependent urban systems (e.g., distributed energy resources, building consumption, electric vehicles)

Each of these computational frameworks and the underlying open datasets have been made available through our lab?s website and/or Github repository (https://www.uil.stanford.edu/data-code) in order to catalyze further research in the area of data-driven urban systems. Additionally, this project helped train an advanced scientific workforce capable of designing and managing our future cities and communities through pedagogical integration of a "living lab" into MS and PhD course offerings at Stanford University including a new interdisciplinary course on urban systems modeling (CEE 243: Intro to Urban Systems Engineering). Students and post-docs who participated on this project also had the opportunity to collaborate with a variety of stakeholders ranging from other academic institutions to industrial affiliates to civic partners via the INTERN program (City of Palo Alto, Silicon Valley Clean Energy). Through a combination of these experiences as well as technical and communication training, these students will enter the workforce and contribute to the burgeoning disciplines of urban informatics and smart cities/communities.

In the end, this project contributed computational frameworks for studying the coupled dynamics of urban systems that represents a radical shift away from viewing urban systems as purely technical and independent systems with a goal of gaining insights on how to better design and operate our cities. With the world and the United States urbanizing, gaining a deeper understanding of urban infrastructure and other urban systems can help ensure that our cities enable all of humanity to flourish and prosper.

 

 


Last Modified: 01/25/2021
Modified by: Rishee Jain

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