Short-Term Wind Power Prediction for Wind Turbine via Kalman Filter Based on JIT Modeling

Tomoki Ishikawa, Takaaki Kojima, Toru Namerikawa

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper addresses wind power prediction, which is known to be a key technology in energy management systems. In this paper, a 24-h-ahead power prediction method using a filter theory is proposed for wind power generation. The prediction method is a simple algorithm. The procedure of prediction consists of two steps: the data processing and the calculation of the predicted values. In data processing, in order to obtain the correlative data from the database, we employ just-in-time modeling. In the calculation of the predicted values, we propose a regression model for wind speed and wind power, and the unknown parameters are estimated using a constrained Kalman filter. Moreover, in the procedure used to estimate the unknown parameters, reduction and convergence of the variables are also guaranteed. Finally, the advantages of the proposed method over the conventional method are shown through actual prediction evaluations.

Original languageEnglish
Pages (from-to)86-96
Number of pages11
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Volume198
Issue number3
DOIs
Publication statusPublished - 2017 Feb 1

Fingerprint

Kalman filters
Wind turbines
Wind power
Energy management systems
Power generation

Keywords

  • constrained Kalman filter
  • energy management system (EMS)
  • JIT modeling
  • short-term prediction
  • wind power

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Short-Term Wind Power Prediction for Wind Turbine via Kalman Filter Based on JIT Modeling. / Ishikawa, Tomoki; Kojima, Takaaki; Namerikawa, Toru.

In: Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), Vol. 198, No. 3, 01.02.2017, p. 86-96.

Research output: Contribution to journalArticle

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