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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kusiak, A. Haiyang Zheng |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Mech. & Ind. Eng., Univ. of Iowa, Iowa City, IA (Kusiak, A.; Haiyang Zheng) |
| 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. |
| Starting Page | 1099 |
| Ending Page | 1103 |
| File Size | 527580 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424418879 |
| DOI | 10.1109/ICSET.2008.4747170 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-24 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Power ramp rate prediction wind farm Weather forecasting Predictive models Data mining Wind forecasting Power system modeling parameter selection Wind energy generation multivariate time series model Wind speed data-mining algorithms Wind farms Wind power generation Power generation |
| Content Type | Text |
| Resource Type | Article |
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