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

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力侧重于对最佳能源决策的数据驱动支持。该项目提出的软件平台将允许对一种可访问、用户友好的工具进行商业部署，以快速确定能源决策对人类健康和生态系统影响的更全面的了解。通过开发面向公众的“影响跟踪器”，该解决方案将为领导人提供一种向公众和具有气候意识的国际投资者传达其能源决策影响的手段，提高公众的能源素养和参与度，并提高美国的经济竞争力。这个小企业创新研究第一阶段项目建议开发一个商业软件平台来支持最佳能源决策。大公司和政府做出的能源决策对人类健康、生态系统质量和生物多样性灭绝具有重大影响。由于收集大量不同的非标准化数据集并评估所需的多个复杂模型所需的时间、数据和财务资源，这些决策的生命周期影响通常无法获得。为了克服这些限制，该团队将开发一个基于云的影响分析软件平台，方法是：1) 构建一个与广泛的地理位置和电网配置兼容的综合能源系统优化和生命周期评估模型；2) 开发一个数据集成工具，用于从多个非标准化、通常是国际托管的数据库自动收集所需数据。这项工作的预期结果将是一个一流的、易于使用且高度可访问的软件平台，该平台在不同的地理区域和电网配置中都是准确的，从而使该工具能够对国家和全球产生影响。克服这些挑战需要将机器学习方法与人类参与相结合，即专家增强机器学习。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

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.