SBIR Phase I: Proximate Wind Forecasts: A New Machine Learning Approach to Increasing Wind Energy Production

SBIR 第一阶段：风力预测：增加风能产量的新机器学习方法

• 批准号: 2309367
• 负责人: Jason Yosinski
• 金额: $ 27.43万
• 依托单位: WINDSCAPEAI LLC
• 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: 2023-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309367&HistoricalAwards=false
• 关键词: SBIR; Phase; Proximate; Wind; Forecasts

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project will be to demonstrate the potential to increase (by 2%) wind-energy production from existing wind farms at very low cost. Combining networked, air-pressure sensors distributed on the landscape with artificial intelligence/machine learning (AI/ML), the technology will empower wind farm operators with advance alerts of oncoming winds and gusts to preemptively adjust settings like blade pitch and turbine yaw. These adjustments will result in more wind energy production and less turbine damage. This technology will significantly increase energy revenues and decrease costs. In 2022, US wind farms produced 380 terawatt hours (TWh) of energy. If serving just half of existing plants, this technology could yield an additional 3.8 TWh of renewable energy and over $150 million to US wind energy sales annually. In the competitive wind industry, these revenues can greatly increase operating margins and help accelerate the growth of the industry and clean energy jobs. Using government emissions figures, this deployment would also avert 2.4 gigatons of carbon dioxide (GTCO2) over 20 years. This wind alert technology could also benefit solar tracker safety and increase safety at aerial vehicle ports and lift-crane operations.This Small Business Innovation Research (SBIR) Phase I project will show how wind can be measured and predicted 10–600 seconds in the future by combining a new sensor modality — distributed pressure sensors — with new machine learning (ML) models. Pressure sensors are far cheaper than wind sensors (e.g., Doppler LIDAR), but processing data from pressure sensors into predictions of the wind is complex. It is impossible to hand-code statistical models to predict turbine-height wind from ground-level pressure measurements. Instead, one may rely on learned ML models to make these predictions. Previous studies have used ML to model weather on regional or global scales, but this project is the first to create models for the much smaller and more demanding scales applicable to wind farm operation and to optimize for metrics important to wind farm operators. Because ML models have not yet been developed directly for combined pressure and wind data at this spatial and temporal scale, this project will combine advances in attention-based models (like Transformers) with advances in models that respect physical priors (like Hamiltonian Neural Networks) and will lead to a new form of sensing which will be far more accurate than was previously possible at this price point.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阶段项目的广播/商业影响将证明现有风电场以非常低的成本增加（提高2％）风能生产的可能性。该技术结合了在景观上分布在景观上的网络，空气接种传感器（AI/ML），该技术将使风电场操作员有能力，并提前警报即将来临的风和阵风，以预先调整刀片俯仰和涡轮偏航等设置。这些调整将导致更多的风能产生和减少涡轮机损坏。该技术将大大增加能源揭示并降低成本。 2022年，美国风电场生产了380 Terawatt小时（TWH）的能源。如果仅提供现有工厂的一半，则该技术每年可能会产生3.8 TWH的可再生能源，每年为美国风能销售提供超过1.5亿美元。在竞争性风能行业中，这些揭示可以大大提高营业利润率，并有助于加速行业的增长和清洁能源就业机会。使用政府的排放数据，这种部署还将在20年内避免2.4千克二氧化碳（GTCO2）。这种风警报技术还可以使太阳能跟踪器的安全有益于太阳能跟踪器的安全性，并提高航空车端口和升降机操作的安全性。该小型企业创新研究（SBIR）I阶段项目将显示如何通过组合新的传感器方式 - 与新的压力传感器与新机器学习（ML）模型相结合，将来如何测量风和预测10-600秒。压力传感器比风传感器（例如，多普勒激光雷达）便宜得多，但是从压力传感器到风的预测数据很复杂。无法手动统计模型从地面压力测量中预测涡轮高的风。取而代之的是，人们可能会依靠学习的ML模型来做出这些预测。先前的研究已经使用ML来模拟区域或全球量表的天气，但是该项目是第一个为适用于风电场运营的较小且苛刻的尺度创建模型的模型，并优化对风电场运营商重要的指标。由于ML模型尚未直接开发用于在这个空间和临时范围内的压力和风数据，因此该项目将结合基于注意力的模型（例如变形金刚）的进步与尊重身体先验（例如Hamiltonian神经网络）（例如Hamiltonian神经网络）的进步的进步，并将导致一种新形式的传感形式，这将比以前的Proite Point and Proime更准确。基金会的智力优点和更广泛的影响评论标准。