SBIR Phase I: Reducing Numerical Weather Forecasting Computational Expense Using Machine Learning

这个小型企业创新研究(SBIR)项目的更广泛影响/商业潜力是开发人工智能(AI)天气预报工具，以提高预报精度并增强电网的弹性。美国目前正在向越来越多的以可再生能源为基础的经济转型。主要的可再生能源太阳能和风能的可获得性随着天气的变化而变化，这使得预报对于计划日常工厂和能源电网运营至关重要。当天气预报错误时，电网效率低下，导致电价飙升，损害消费者利益。在世界范围内，国家气象机构不断向公众发布天气数据。该项目论证了通过实时收集、组织和利用大型公共天气数据集来改进天气预报的可行性。人工智能将整理这些数据，为能源行业以及其他需要高保真预报的商业消费者提供先进的天气洞察。这些见解将使能源市场参与者能够预测市场走势，纠正低效，并导致更具弹性的能源电网和电力分配。这个SBIR第一阶段项目提议通过用人工智能增强基于物理的大气模型来提高天气预报的状态。该项目的智力优势是开发了一个算法工具包，该工具包实时应用人工智能来检测天气预报中的错误，并使用机器学习访问历史预报以提供纠错。该项目的目标是1)开发与德克萨斯州主要风电场地点相对应的历史风力预报数据数据库；2)培训和验证人工神经网络、随机森林和概率模拟等人工智能算法集合，以检测历史预报数据中的错误；以及3)开发软件，从基于物理的模型中获取公开可用的天气预报，应用人工智能算法，并实时提供先进的天气预报见解。这项研究将提供可行性，即人工智能算法可以轻松地与高保真模型集成，以补充现有的天气预报基础设施。在这个项目中开发的技术是一个通用的环境预测框架，它展示了AI/ML应用程序在一系列与天气有关的科学和工程挑战中的使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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) project is the development of artificial intelligence (AI) weather forecasting tools to improve forecast accuracy and strengthen power grid resiliency. The United States is currently transitioning to an increasingly renewable energy-based economy. The availability of the leading renewable sources, solar and wind, vary with weather, making forecasts critical to plan for daily plant and energy grid operations. When weather forecasts are wrong, the grid suffers inefficiencies, causing power prices to spike and hurt consumers. Worldwide, national weather agencies continually release weather data to the public. This project demonstrates the feasibility of improving weather forecasting by collecting, organizing, and leveraging large public weather datasets in real-time. AI will sort through these data to deliver advanced weather insights to the energy industry as well as other commercial consumers requiring high fidelity forecasts. These insights will allow energy market participants to anticipate market movements, correct inefficiencies, and lead to a more resilient energy grid and power distribution.This SBIR Phase I project proposes to advance the state of weather forecasting by augmenting physics-based atmospheric models with artificial intelligence. The intellectual merit of this project is the development of a toolkit of algorithms that in real-time applies AI to detect errors in weather forecasts and uses machine learning accessing historical forecasts to provide error correction. The project’s objectives are 1) development of a database of historical wind forecast data corresponding to major Texas wind farm sites; 2) training and validation of an ensemble of AI algorithms, e.g. artificial neural networks, random forests, and probabilistic analogs, that detect errors in historical forecast data; and 3) development of software that ingests publicly available weather forecasts from physics-based models, applies AI algorithms, and delivers advanced weather forecast insights in real-time. This research will provide feasibility that AI algorithms can easily integrate with high-fidelity models to complement the existing weather forecasting infrastructure. The technology developed in this project is a general environmental forecasting framework that demonstrates the use of AI/ML applications to a broad set of weather-dependent scientific and engineering challenges.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.