
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
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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 2019 = $55,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
450 JANE STANFORD WAY STANFORD CA US 94305-2004 (650)723-2300 |
Sponsor Congressional District: |
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Primary Place of Performance: |
473 Via Ortega Stanford CA US 94305-4027 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
S&CC: Smart & Connected Commun, GOALI-Grnt Opp Acad Lia wIndus |
Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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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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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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