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Research Papers

Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach

[+] Author and Article Information
Haiyang Zheng

Department of Mechanical and Industrial Engineering, 3131 Seamans Center, University of Iowa, Iowa City, IA 52242-1527

Andrew Kusiak

Department of Mechanical and Industrial Engineering, 3131 Seamans Center, University of Iowa, Iowa City, IA 52242-1527andrew-kusiak@uiowa.edu

J. Sol. Energy Eng 131(3), 031011 (Jul 09, 2009) (8 pages) doi:10.1115/1.3142727 History: Received August 10, 2008; Revised March 06, 2009; Published July 09, 2009

In this paper, multivariate time series models were built to predict the power ramp rates of 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 algorithms were 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 of the 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.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Typical power, power ramp rate, and wind speed plots: (a) wind farm power, (b) power ramp rate, and (c) wind speed

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Figure 2

The importance of predictors generated by the boosting tree algorithm for the t+10 model

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Figure 3

Illustration of the multiperiod multivariate time series prediction model: (a) the t+10 minPRR prediction and (b) the t+20 minPRR prediction

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Figure 4

Prediction results produced by the t+10 model without parameter selection: (a) prediction performance of the five different algorithms for the test data set of Table 2 and (b) the observed and predicted PPRs by the SVM algorithm

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Figure 5

The prediction results of the t+10 model with parameter selection: (a) prediction performance of the five algorithms for the test data set of Table 2 and (b) observed and predicted PRRs by the SVM algorithm

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Figure 6

The importance of predictors computed by the boosting tree algorithm

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Figure 7

Observed and predicted PRRs from the t+20 models with selected parameters: (a) MLP algorithm, (b) SVM algorithm, (c) random forest algorithm, (d) C&R tree algorithm, and (e) pace regression algorithm

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Figure 8

Observed and predicted PRRs for different periods for the first 100 test data points: (a) the t+30 minPRR model, (b) the t+40 minPRR model, (c) the t+50 minPRR model, and (d) the t+60 minPRR model

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