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

13th International Conference on Motion and Vibration Control; 12th International Conference on Recent Advances in Structural Dynamics

Urata, Kengo^1 ; Inoue, Masaki^1 ; Murayama, Dai^2 ; Adachi, Shuichi^1

Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Yokohama, Kohoku-ku

223-8522, Japan^1

Toshiba Corporation Power Systems Company, 2-4 Suehiro-cho, Yokohama, Tsurumi-ku

230-0045, Japan^2

关键词: Auto regressive models;    Box Cox transformation;    Cascade structures;    Gaussian white noise;    Output distribution;    Prediction accuracy;    Short term prediction;    Statistical modeling;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/744/1/012176/pdf
DOI  :  10.1088/1742-6596/744/1/012176

来源: IOP

【 摘 要 】

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.