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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛/商业影响是创建了一个广泛的(1,000个成果)、超局部(不到3公里)的气候模拟档案，电网规划者和能源行业投资者可以使用它来更好地了解电网可靠性和可再生能源资产生存能力的前瞻性风险。这一模拟数据将针对德克萨斯州电子可靠性委员会(ERCOT)电网内的所有地点进行预先计算，使规划者和投资者能够快速建模不同可再生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电网和更快的能源过渡，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。该小型企业创新研究(SBIR)第一阶段项目建议创建一个气候模拟引擎，该引擎可以为许多地点和许多天气变量生成合成的每小时本地天气模式(所有这些都是对电力需求、风力发电和太阳能发电等能源进行建模所必需的)。该项目将不依赖于基于物理的全球气候模型，因为这些模型的计算强度，以及需要对当地而不是区域或全球天气进行建模。相反，该项目将研究统计模拟与人工智能(AI)的创新结合，利用两者的优势互补对方的弱点。例如，统计仿真模型是精确的，但不能缩放，而AI仿真模型可以几乎无限制地缩放，但不精确。该项目研究将探索一种新的方法，以提高人工智能模式生成的精确度(通过已知的统计数据)，从而产生规模上的高保真气候模型。该项目的预期技术成果是创建一个模拟引擎，它可以模拟德克萨斯州每小时超局部天气的1000个结果--精度类似于纯统计模型基准，同时保持云计算资源的低成本。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/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.