2230578
Project Grant
Overview
Grant Description
Sbir Phase I: An Impact Analytics Platform Combining Energy System Optimization and Life Cycle Assessment -The Broader Impact/Commercial Potential of This Small Business Innovation Research (Sbir) Phase I Project Focuses on Data-Driven Support for Optimal Energy Decisions.
The Software Platform Proposed in This Project Will Allow for Commercial Deployment of an Accessible, User-Friendly Tool to Rapidly Determine a More Complete Picture of Human Health and Ecosystem Impacts as a Result of Energy Decisions.
Through the Development of a Public-Facing ?Impact Tracker,? This Solution Will Provide a Means for Leaders to Communicate the Impacts of Their Energy Decisions to the Public and Climate-Conscious International Investors, Improving the Public?s Energy Literacy and Engagement, as Well as Increasing the Economic Competitiveness of the United States.
This Small Business Innovation Research Phase I Project Proposes to Develop a Commercial Software Platform to Support Optimal Energy Decisions.
Energy Decisions Made by Large Corporations and Governments Have Substantial Impacts on Human Health, Ecosystem Quality, and Biodiversity Extinction.
The Life Cycle Impacts of These Decisions Are Often Inaccessible Due to the Time, Data and Financial Resources Required to Collect the Numerous, Disparate, Non-Standardized Datasets and Evaluate the Multiple Complex Modeling That Is Required.
To Overcome These Limitations, This Team Will Develop a Cloud-Based, Impact Analytics Software Platform by 1) Building an Integrated Energy System Optimization and Life Cycle Assessment Model That Is Compatible with a Broad Range of Geographies and Electricity Grid Configurations and 2) Developing a Data Integration Tool for Automated Collection of the Required Data from Multiple Non-Standardized, Often Internationally Housed Databases.
The Anticipated Results of This Work Will Be a First-In-Class, Easy-to-Use, and Highly Accessible Software Platform That Is Accurate Across Varying Geographic Regions and Electricity Grid Configurations, Allowing for This Tool to Have National and Global Impacts.
Overcoming These Challenges Will Require a Combination of Machine Learning Approaches with Human Involvement, Known as Expert-Augmented Machine Learning.
This Award Reflects Nsf's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.
The Software Platform Proposed in This Project Will Allow for Commercial Deployment of an Accessible, User-Friendly Tool to Rapidly Determine a More Complete Picture of Human Health and Ecosystem Impacts as a Result of Energy Decisions.
Through the Development of a Public-Facing ?Impact Tracker,? This Solution Will Provide a Means for Leaders to Communicate the Impacts of Their Energy Decisions to the Public and Climate-Conscious International Investors, Improving the Public?s Energy Literacy and Engagement, as Well as Increasing the Economic Competitiveness of the United States.
This Small Business Innovation Research Phase I Project Proposes to Develop a Commercial Software Platform to Support Optimal Energy Decisions.
Energy Decisions Made by Large Corporations and Governments Have Substantial Impacts on Human Health, Ecosystem Quality, and Biodiversity Extinction.
The Life Cycle Impacts of These Decisions Are Often Inaccessible Due to the Time, Data and Financial Resources Required to Collect the Numerous, Disparate, Non-Standardized Datasets and Evaluate the Multiple Complex Modeling That Is Required.
To Overcome These Limitations, This Team Will Develop a Cloud-Based, Impact Analytics Software Platform by 1) Building an Integrated Energy System Optimization and Life Cycle Assessment Model That Is Compatible with a Broad Range of Geographies and Electricity Grid Configurations and 2) Developing a Data Integration Tool for Automated Collection of the Required Data from Multiple Non-Standardized, Often Internationally Housed Databases.
The Anticipated Results of This Work Will Be a First-In-Class, Easy-to-Use, and Highly Accessible Software Platform That Is Accurate Across Varying Geographic Regions and Electricity Grid Configurations, Allowing for This Tool to Have National and Global Impacts.
Overcoming These Challenges Will Require a Combination of Machine Learning Approaches with Human Involvement, Known as Expert-Augmented Machine Learning.
This Award Reflects Nsf's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Santa Barbara,
California
93101-1875
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Quantum Energy was awarded
Project Grant 2230578
worth $255,960
from National Science Foundation in July 2023 with work to be completed primarily in Santa Barbara California United States.
The grant
has a duration of 7 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:An impact analytics platform combining energy system optimization and life cycle assessment
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project focuses on data-driven support for optimal energy decisions. The software platform proposed in this project will allow for commercial deployment of an accessible, user-friendly tool to rapidly determine a more complete picture of human health and ecosystem impacts as a result of energy decisions. Through the development of a public-facing ‘Impact Tracker,’ this solution will provide a means for leaders to communicate the impacts of their energy decisions to the public and climate-conscious international investors, improving the public’s energy literacy and engagement, as well as increasing the economic competitiveness of the United States. _x000D_ _x000D_ This Small Business Innovation Research Phase I project proposes to develop a commercial software platform to support optimal energy decisions. Energy decisions made by large corporations and governments have substantial impacts on human health, ecosystem quality, and biodiversity extinction. The life cycle impacts of these decisions are often inaccessible due to the time, data and financial resources required to collect the numerous, disparate, non-standardized datasets and evaluate the multiple complex modeling that is required. To overcome these limitations, this team will develop a cloud-based, impact analytics software platform by 1) building an integrated energy system optimization and life cycle assessment model that is compatible with a broad range of geographies and electricity grid configurations and 2) developing a data integration tool for automated collection of the required data from multiple non-standardized, often internationally housed databases. The anticipated results of this work will be a first-in-class, easy-to-use, and highly accessible software platform that is accurate across varying geographic regions and electricity grid configurations, allowing for this tool to have national and global impacts. Overcoming these challenges will require a combination of machine learning approaches with human involvement, known as expert-augmented machine learning._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
EN
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 7/18/23
Period of Performance
7/1/23
Start Date
2/29/24
End Date
Funding Split
$256.0K
Federal Obligation
$0.0
Non-Federal Obligation
$256.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2230578
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
P7BRFUQBRN23
Awardee CAGE
957T2
Performance District
24
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Representative
Salud Carbajal
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $255,960 | 100% |
Modified: 7/18/23