Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting

@inproceedings{Barber2010AutoRegressiveHI,
  title={Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting},
  author={Chris Barber and Joseph Bockhorst and Paul Roebber},
  booktitle={NIPS},
  year={2010}
}
Accurate short-term wind forecasts (STWFs), with time horizons from 0.5 to 6 hours, are essential for efficient integration of wind power to the electrical power grid. Physical models based on numerical weather predictions are currently not competitive, and research on machine learning approaches is ongoing. Two major challenges confronting these efforts… CONTINUE READING