Prediction of wind farm power ramp rates: A data-mining approach
Journal of Solar Energy Engineering, Transactions of the ASME
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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