Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation. (September 2016)

• Record Type: Journal Article
• Authors: Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi
• Is Part Of: Journal of physics. Volume 744(2016)
• Publication Date: 2016-09
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/744/1/012176
• Languages: English
• ISSNs: 1742-6588

Abstract

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.