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.
