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

Haiyang Zheng Andrew Kusiak e-mail: [e-mail] Department of Mechanical and Industrial Engineering, 3131 Seamans Center, University of Iowa, Iowa City, IA 52242-1527

Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach
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. DOI: 10.1115/1.3142727 Keywords: power ramp rate prediction, wind farm, data-mining algorithms, multivariate time series model, parameter selection

1 Introduction
Wind power generation is rapidly expanding and is becoming a noticeable contributor to the electric grid. The fact that most largescale wind farms were developed in recent years has made studies of their performance overdue. Given the changing nature of the wind regime, wind farm power varies across all time scales. The fluctuating power of wind farms is usually balanced by the power produced by the traditional power plants to meet the grid requirements. The change of power output in time is referred to as ramping and it is measured with the power ramp rate PRR . The prediction of PRR at 10 min intervals is of interest to the wind industry due to the tightening electric grid requirements 1 . Though the power prediction research has a long

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