Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

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 ot...

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Bibliographic Details
Published in:Journal of physics. Conference series 2016-09, Vol.744 (1), p.12176
Main Authors: Urata, Kengo, Inoue, Masaki, Murayama, Dai, Adachi, Shuichi
Format: Article
Language:English
Subjects:
Autoregressive models
Dynamic models
Physics
Statistical models
White noise
Wind power
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Summary: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.
ISSN:1742-6588
1742-6596
1742-6596
DOI:10.1088/1742-6596/744/1/012176