Title

Prediction of wind farm power ramp rates: A data-mining approach

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2009

Journal/Book/Conference Title

Journal of Solar Energy Engineering, Transactions of the ASME

Volume

131

Abstract

In this paper, multivariate time series models were built to predict the power ramp ratesof a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithmswere tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10-60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy ofthe power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided. 2009 by ASME.

Keywords

Sustainability, In this paper, multivariate time series models were built to predict the power ramp ratesof a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithmswere tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10-60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy ofthe power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided. 2009 by ASME.

Published Article/Book Citation

Journal of Solar Energy Engineering, Transactions of the ASME, 131:3 (2009) pp.310111-310118.



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URL

http://ir.uiowa.edu/cee_pubs/514