AI/ML Augmented Hyper Local Weather Forecast

Our very recent artificial intelligence – machine learning (AI/ML) augmented wind energy production forecast has successfully demonstrated a consistent >30% wind speed and power generation forecast improvement over the NOAA operational High-Resolution Rapid Refresh (HRRR) standalone capability.

So far, we have used a suite of AI/ML algorithms, including 1) artificial neural networks, 2) ridge regression, 3) lasso regression, 4) support vector machines, 5) gradient boosting, 6) elastic networks, 7) nearest neighboring clustering, and 8) random forest (RF) models for wind energy forecasting applications. These AI/ML augmented forecasts significantly improve the management of the power grid distribution, energy trading strategy, and plant operations with training and testing corresponding to 253 sites in Texas and validated on a year of independent testing data. It has shown that each AI/ML model offers significant forecast improvement (+20% mean squared error) skill over the current official HRRR forecasts. Furthermore, an AI/ML model ensemble of different machine learning models is deployed and demonstrated to significantly improve wind speed accuracy during all seasons, times of day, sites tested, and forecast horizon times.

This fully matured AI/ML augmented framework has shown to be comprehensive and robust, demonstrating that AI/ML is a natural complement to the existing NWP infrastructure and can be expanded to enhance local forecasts.