We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 2016 Oct 3|
|Event||13th International Conference on Motion and Vibration Control, MOVIC 2016 and the 12th International Conference on Recent Advances in Structural Dynamics, RASD 2016 - Southampton, United Kingdom|
Duration: 2016 Jul 4 → 2016 Jul 6
ASJC Scopus subject areas
- Physics and Astronomy(all)