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

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._x000D_ _x000D_ 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._x000D_ _x000D_ 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.