Title

Data mining for prediction of wind farm power ramp rates

Document Type

Conference Paper

Peer Reviewed

1

Publication Date

1-1-2008

Journal/Book/Conference Title

2008 IEEE International Conference on Sustainable Energy Technologies, ICSET 2008, November 24, 2008 - November 27

Conference Location

Singapore, Singapore

Abstract

In this paper, multivariate time series models are built to predict the power ramp rate of a wind farm. The power changes are predicted at ten-minute intervals. Multivariate time series models are built with data-mining algorithms. Five different data-mining algorithms are tested using data collected at a wind farm. The support vector machine regression algorithm performed best of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10 to 60 minutes. The boosting tree algorithm selected predictors enhancing the prediction accuracy. The data used in this research originated at a wind farm of over 100 turbines. The test results of various models are presented in the paper. Suggestions for future research are provided. 2008 IEEE.

Keywords

Sustainability, In this paper, multivariate time series models are built to predict the power ramp rate of a wind farm. The power changes are predicted at ten-minute intervals. Multivariate time series models are built with data-mining algorithms. Five different data-mining algorithms are tested using data collected at a wind farm. The support vector machine regression algorithm performed best of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10 to 60 minutes. The boosting tree algorithm selected predictors enhancing the prediction accuracy. The data used in this research originated at a wind farm of over 100 turbines. The test results of various models are presented in the paper. Suggestions for future research are provided. 2008 IEEE.

Published Article/Book Citation

2008 IEEE International Conference on Sustainable Energy Technologies, ICSET 2008, November 24, 2008 - November 27, Singapore, Singapore, 2008.



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URL

http://ir.uiowa.edu/cee_pubs/480