Abstract
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.
| Original language | English |
|---|---|
| Article number | 012176 |
| Journal | Journal of Physics: Conference Series |
| Volume | 744 |
| Issue number | 1 |
| DOIs | |
| 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)
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Urata, K., Inoue, M., Murayama, D., & Adachi, S. (2016). Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation. Journal of Physics: Conference Series, 744(1), [012176]. https://doi.org/10.1088/1742-6596/744/1/012176