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A Wind Power Forecasting System to Optimize Grid Integration

Sources:

IEEE

Authors:

William P. Mahoney

Keith Parks

Gerry Wiener

Yubao Liu

William L. Myers

Juanzhen Sun

Luca Delle Monache

Thomas Hopson

David Johnson

Sue Ellen Haupt

Date:

1 October 2012

This wind power forecasting system includes high-resolution and ensemble modeling capabilities, data assimilation, now-casting and statistical post-processing technologies. The system uses publicly available model data and observations as well as wind forecasts produced from an National Center for Atmospheric (NCAR)-developed deterministic mesoscale wind forecast model with real-time four-dimensional data assimilation and a 30-member model ensemble system, which is calibrated using an Analogue Ensemble Kalman Filter and Quantile Regression.