Improving Short-Term Wind Power Forecasting Through Measurements and Modeling of the Tehachapi Wind Resource Area

This report describes atmospheric measurements and modeling in and around the Tehachapi Wind Resource Area to improve wind power forecasting in the short term (0-15 hour ahead) and very short term (0-3 hours ahead). The measurement component of the project involved maintaining and expanding a network of sensors targeted at improving wind speed forecasts in the Tehachapi Wind Resource Area. When combined with earlier measurements, this network produced a 25-month dataset of wind speeds, temperature, humidity, and other relevant parameters in the Tehachapi Pass. The modeling component used mechanical learning techniques to develop predictors for very short-term forecasts. An initial study of model configurations led to the selection of an appropriate set of submodels for the Tehachapi Wind Resource Area. These submodels, which included a wind turbine drag submodel, had a significant effect on the mean absolute error and bias of the wind power forecasts. A machine learning method was used to identify predictors for the 15-minute average power production, 60-minute ramp rates, and occurrence of large ramp events in the 0-3 hour look-ahead period. The forecasting improvements developed as part of this project, including data from project sensors, improved data assimilation methods, and machine-learning-based predictors were combined in the improved operational forecast system. In a six-month forecast evaluation, the improved system achieved a 13.5 percent reduction in the mean absolute error of the 15-minute average power production in the Tehachapi Wind Resource Area against an optimized ensemble of National Weather Service forecast models.