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

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

https://ir.uiowa.edu/mie_pubs/41