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

Kengo Urata, Masaki Inoue, Dai Murayama, Shuichi Adachi

Research output: Contribution to journalConference article

1 Citation (Scopus)

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 languageEnglish
Article number012176
JournalJournal of Physics: Conference Series
Volume744
Issue number1
DOIs
Publication statusPublished - 2016 Oct 3
Event13th 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 42016 Jul 6

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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