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.

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响将是展示结合联网气压传感器以极低的成本将现有风电场的风能产量提高（2%）的潜力。该技术通过人工智能/机器学习（AI/ML）分布在景观中，使风电场运营商能够提前发出迎面风和阵风的警报，从而预先调整叶片桨距和涡轮机偏航等设置。该技术将显着增加能源收入并降低成本，到 2022 年，美国风电场的发电量将达到 380 太瓦时 (TWh)，如果仅服务于现有发电厂的一半，则该技术可额外产生 3.8 太瓦时的发电量。太瓦时的可再生能源和每年超过 1.5 亿美元的美国风能销售额在竞争激烈的风能行业中，这些收入可以大大增加运营利润，并有助于加速该行业和清洁能源就业的增长。 20 年内，这种风警报技术还可以减少 2.4 亿吨二氧化碳 (GTCO2) 的排放，并有利于太阳能跟踪器的安全性，并提高飞行器港口和起重机操作的安全性。小型企业创新研究 (SBIR) 第一阶段项目将展示这一点。如何通过将新的传感器模式（分布式压力传感器）与新的机器学习 (ML) 模型相结合来测量和预测未来 10-600 秒的风。压力传感器比风传感器便宜得多（例如，多普勒激光雷达），但将压力传感器的数据处理为风的预测是很复杂的，不可能手动编写统计模型来根据地面压力测量来预测涡轮机高度风。之前的研究已经使用机器学习在区域或全球范围内对天气进行建模，但该项目是第一个为适用于风电场运营的更小且要求更高的规模创建模型的项目，并优化对风电场运营商重要的指标。 .因为ML模型还没有然而，该项目是直接针对这种空间和时间尺度上的组合压力和风数据而开发的，该项目将结合基于注意力的模型（如 Transformers）的进步与尊重物理先验的模型（如哈密顿神经网络）的进步，并将导致一种新的传感形式，在这个价位上比以前更准确。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。