SBIR Phase I: An impact analytics platform combining energy system optimization and life cycle assessment

SBIR 第一阶段：结合能源系统优化和生命周期评估的影响分析平台

• 批准号: 2230578
• 负责人: Daniel Howard
• 金额: $ 25.6万
• 依托单位: QUANTUM ENERGY, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230578&HistoricalAwards=false
• 关键词: SBIR; Phase; impact; analytics; platform

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.

我项目的更广泛的影响力/商业电位集中在该项目中的软件平台支撑物上，这将是数据驱动的Suptir。通过开发“影响追踪器”的能源决策，人类健康和生态系统会影响，该解决方案将为领导者提供一种能源决策的影响公众的素养和参与度，美国的最佳能源决策，影响分析软件平台1）建立一个集成的能源系统优化和生命周期评估模型，该模型是兼容的广泛地理和电网配置，以及2）开发一个数据集成工具，用于自动收集所需的非标准化，未标准化的，国际上的数据库通常是Wilk Wilk Wilk wilk，易于使用的平台，并且准确的平台变化了不同的地理位置和电气网格配置。 Ment，被称为专家的机器学习。该奖项反映了NSF的fly命令，并值得支持评估USATI ONTELECTUAL CORITUAL和更广泛的影响标准。