Award Abstract # 1461549
SEES Fellows: Building Informatics: Utilizing Data-Driven Methodologies to Enable Energy Efficiency and Sustainability Planning of Urban Building Systems

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date: September 10, 2014
Latest Amendment Date: July 27, 2017
Award Number: 1461549
Award Instrument: Standard Grant
Program Manager: Alan Sussman
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2014
End Date: August 31, 2019 (Estimated)
Total Intended Award Amount: $528,179.00
Total Awarded Amount to Date: $628,179.00
Funds Obligated to Date: FY 2014 = $528,179.00
FY 2017 = $100,000.00
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
CA  US  94304-1212
Primary Place of Performance
Congressional District:
16
Unique Entity Identifier (UEI): HJD6G4D6TJY5
Parent UEI:
NSF Program(s): EDUCATION AND WORKFORCE,
SEES Fellows,
CyberSEES
Primary Program Source: 01001415DB NSF RESEARCH & RELATED ACTIVIT
01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 018Z, 7231, 7361, 8055, 8060, 8232, 9102, 9179
Program Element Code(s): 736100, 805500, 821100
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The project is supported under the NSF Science, Engineering and Education for Sustainability Fellows (SEES Fellows) program, with the goal of helping to enable discoveries needed to inform actions that lead to environmental, energy and societal sustainability while creating the necessary workforce to address these challenges. Sustainability science is an emerging field that addresses the challenges of meeting human needs without harm to the environment, and without sacrificing the ability of future generations to meet their needs. A strong scientific workforce requires individuals educated and trained in interdisciplinary research and thinking, especially in the area of sustainability science. With the SEES Fellowship support, this project will enable a promising early career researcher to establish himself in an independent research career related to sustainability.

This project addresses the fact that buildings are responsible for over 40% of all energy consumption and GHG emissions in the US. The impact of building systems is even more pronounced in dense urban areas like New York City (NYC) where over 75% of GHG emissions come from energy used in buildings. Any transition towards a sustainable NYC will require addressing this energy and pollution intensive built environment. This research proposal aims to integrate data-driven methodologies from information science, social science, network science and urban planning with engineering to increase the energy efficiency and sustainability of urban building systems. Specifically, this project will identify district level energy efficiency opportunities for urban buildings systems, characterize the interdependencies between the built, natural and human environments and model the impact of energy policy instruments to enhance the sustainability and resilience of NYC and Mumbai, India. As a part of this project, the PI will build upon his expertise in civil engineering and building systems by incorporating data science, social science and urban planning into his exploration of energy and sustainability challenges facing urban building systems.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 20)
Alex Nutkiewicz, Zheng Yang, Rishee K. Jain "Data-driven Urban Energy Simulation (DUES): Integrating machine learning into an urban building energy simulation workflow" 9th International Conference on Applied Energy, ICAE2017 , 2017
Andrew Sonta, Rishee K. Jain, Rimas Gulbinas, Jose M.F. Moura, John E. Taylor "OESP_G: A Computational Framework for Multidimensional Analysis of Occupant Energy Use Data in Commercial Buildings" ASCE Journal of Computing in Civil Engineering , v.31 , 2017 https://doi.org/10.1061/(ASCE)CP.1943-5487.0000663
Debnath, R, Bardhan, R., Jain, R. K. "A Data-Driven and Simulation Approach for Understanding Thermal Performance of Slum Redevelopment in Mumbai, India" 2017 Building Simulation Conference (IBPSA) , 2017
Khosrowpour, A., Jain, R.K., Taylor, J.E., Peschiera, G., Chen, J., & Gulbinas, R. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation" Applied Energy , v.218 , 2018 https://doi.org/10.1016/j.apenergy.2018.02.148
Kontokosta, Constantine E and Jain, Rishee K "Modeling the determinants of large-scale building water use: Implications for data-driven urban sustainability policy" Sustainable Cities and Society , v.18 , 2015 , p.44--55
Nutkiewicz, A., Jain, R.K., Bardhan, R. "Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India" Applied Energy , 2018 https://doi.org/10.1016/j.apenergy.2018.09.002
Nutkiewicz, A., Yang, Z., & Jain, R. 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 https://doi.org/10.1016/j.apenergy.2018.05.023
Ramit Debnath, Ronita Bardhan, Rishee K. Jain "A data-driven design framework for urban slum housing: Case of Mumbai" ACM International Conference on Embedded Systems for Energy-Efficient Buildings (BuildSys 2016) , 2016 10.1145/2993422.2996406
Rimas Gulbinas, Rishee K. Jain "Towards the development of a visual data exploration tool to augment decision-making in urban building energy efficiency programs" 16th International Conference on Computing in Civil and Building Engineering , 2016
Rishee K. Jain, Junjie Qin, Ram Rajagopal "Data-driven planning of distributed energy resources (DER) amidst socio-technical complexities" Nature Energy , 2017 doi:10.1038/nenergy.2017.112
Roth, J., Bailey, A., Choudhary, S., & Jain, R. K. "Spatial and Temporal Modeling of Urban Building Energy Consumption Using Machine Learning and Open Data" Proceedings of the 2019 ASCE International Conference on Computing in Civil Engineering , 2019
(Showing: 1 - 10 of 20)

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.

This project addresses the challenge of improving the energy efficiency of urban building systems. Given that buildings are responsible for over 40% of all energy consumption and GHG emissions in the US, any transition towards more sustainable cities and communities will require addressing this energy and pollution intensive built environment. 

To tackle this challenge, PI Jain and his team developed a series of innovative models and methods for understanding the energy dynamics of urban building systems and identifying pathways for improving efficiency, reducing environmental impact and enhancing overall sustainability. Specifically, the project team characterized the interdependencies between the built, natural and human environments and modeled the impact that energy policy instruments have on the sustainability of a wide range of communities/cities (e.g., New York, San Francisco, Mumbai). Results of this work identified:

1) possibilities of realizing 50% reduction in the levelized cost of electricity (LCOE) through intelligent distributed energy resource planning and building efficiency measures (ReMatch);

2) scope to develop targeted strategies for deploying energy efficiency incentives and programs (DUE-B);

3) significant opportunities for enhancing usability between data analytics and simulation based tool in the building energy domain;

4) key data variables necessary to include in local/state benchmarking data disclosure policies;

5) zero-training models to understand where/when occupants use energy in a commercial building;

6) design changes required to reduce energy burdens and improve indoor thermal comfort (+360 hours/year now complaint) during informal settlement redevelopment.

Additionally, this project had a significant impact on the educational and professional development of the PI and high-school (2), undergraduate (3) and graduate students (7) engaged on the project. PI Jain developed a new MS course (?Intro to Urban Systems Engineering?) at Stanford University that utilized concepts on modeling interactions between built, natural and human environments that was informed by research undertaken in this project. All students engaged in this project received an interdisciplinary education through course work and independent study that incorporated concepts from data science, social science, urban planning and civil engineering. Students also had the opportunity to collaborate with a variety of stakeholders ranging from other academic institutions to industrial affiliates to national laboratories (INTERN program). 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 building informatics and smart cities/communities.

In the end, as the world rapidly urbanizes (re)-engineering the energy and other infrastructure systems of our cities to be more sustainable is vital to the future of the United States and the world. The discipline areas of building informatics, smart cities/communities and sustainability planning will have a substantial impact on contributing to this transition to more sustainable urban energy systems. This project developed fundamental data models and methods to facilitate and even accelerate this transition to fully realize the sustainability and efficiency benefits of integrated and intelligent urban systems.  By actively engaging in projects here in cities/communities in the United States and in the developing world (India), this project aimed to have an impact on how tomorrow?s generation of urban citizens live and how our cities continue to develop into sustainable and flourishing communities for all.


Last Modified: 12/20/2019
Modified by: Rishee Jain

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