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 |
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ASJC Scopus subject areas
- Physics and Astronomy(all)
Cite this
Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation. / Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi.
In: Journal of Physics: Conference Series, Vol. 744, No. 1, 012176, 03.10.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation
AU - Urata, Kengo
AU - Inoue, Masaki
AU - Murayama, Dai
AU - Adachi, Shuichi
PY - 2016/10/3
Y1 - 2016/10/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84994102662&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994102662&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/744/1/012176
DO - 10.1088/1742-6596/744/1/012176
M3 - Article
AN - SCOPUS:84994102662
VL - 744
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012176
ER -