SBIR Phase I: High Fidelity Climate Simulation Powered by Generative Adversarial Networks

SBIR 第一阶段：由生成对抗网络提供支持的高保真气候模拟

• 批准号: 2335370
• 负责人: Timothy Ivancic
• 金额: $ 27.5万
• 依托单位: SUNAIRIO INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335370&HistoricalAwards=false
• 关键词: SBIR; Phase; Fidelity; Climate; Simulation

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the creation of a broad (1,000 outcome), hyperlocal (less than 3 km) climate simulation archive that can be used by power grid planners and energy industry investors to better understand forward-looking risks to grid reliability and renewable energy asset viability. This simulation data will be pre-computed for all locations within the Electronic Reliability Council of Texas (ERCOT) power grid, enabling planners and investors to quickly model the probabilistic impact of different renewable energy capacity pathways and different electrification trends. Ultimately, this data will support a more reliable grid and faster energy transition because decision-makers will have access to a single source of future weather data that incorporates extreme events, natural variability, and climate change. This Small Business Innovation Research (SBIR) Phase I project proposes the creation of a climate simulation engine that generates synthetic hourly local weather patterns for many locations and many weather variables (all that are needed to model energy resources such as utility demand, wind generation, and solar generation). The project will not rely on physics-based global climate models due to the computational intensity of those models and the need to model local rather than regional or global weather. Instead, this project will research an innovative combination of statistical simulation with artificial intelligence (AI), leveraging the strengths of each to compensate for the weaknesses of the other. For example, statistical simulation models are precise but do not scale, while AI simulation models can scale almost without limit but are not precise. The project research will investigate a new method to impose precision (via known statistics) on AI pattern generation, yielding a high-fidelity climate model at scale. The expected technical result of the project is the creation of a simulation engine that can simulate 1,000 outcomes of hyperlocal hourly weather over the state of Texas--with accuracy similar to a pure-statistics model benchmark while keeping the cost of cloud computing resources low.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.

这项小型企业创新研究（SBIR）I期项目的更广泛/商业影响是创建广泛的（1,000个结果），超本地（小于3公里）的气候模拟档案库，可以通过电力网格规划师和能源行业投资者使用，以更好地理解前瞻性的远景可靠性和可再生能源的能源资源。该模拟数据将预先计算得克萨斯州电子可靠性委员会（ERCOT）电网中的所有位置，使计划人员和投资者能够快速建模不同可再生能源能源容量途径和不同电气化趋势的概率影响。最终，这些数据将支持更可靠的网格和更快的能源过渡，因为决策者将可以访问包含极端事件，自然变异性和气候变化的未来天气数据的单一来源。这项小型企业创新研究（SBIR）I阶段项目提议创建气候模拟引擎，该引擎为许多位置和许多天气变量生成合成的小时当地天气模式（所有这些都需要建模诸如公用事业需求，风能产生和太阳能生成之类的能源资源）。由于这些模型的计算强度以及对本地或全球天气建模的需求，该项目将不依赖基于物理的全球气候模型。取而代之的是，该项目将研究统计模拟与人工智能（AI）的创新组合，利用每个人的优势来补偿对方的弱点。例如，统计仿真模型是精确的，但不扩展，而AI模拟模型几乎可以不限制，但并不精确。该项目研究将研究一种新方法，以对AI模式产生施加精确度（通过已知统计数据），从而在大规模上产生高保真气候模型。 The expected technical result of the project is the creation of a simulation engine that can simulate 1,000 outcomes of hyperlocal hourly weather over the state of Texas--with accuracy similar to a pure-statistics model benchmark while keeping the cost of cloud computing resources low.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.