Conference proceeding
Data mining for prediction of wind farm power ramp rates
2008 IEEE International Conference on Sustainable Energy Technologies, pp.1099-1103
11/2008
DOI: 10.1109/ICSET.2008.4747170
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.
Details
- Title: Subtitle
- Data mining for prediction of wind farm power ramp rates
- Creators
- A Kusiak - University of IowaHaiyang Zheng - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2008 IEEE International Conference on Sustainable Energy Technologies, pp.1099-1103
- Publisher
- IEEE
- DOI
- 10.1109/ICSET.2008.4747170
- ISSN
- 2165-4387
- Language
- English
- Date published
- 11/2008
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9984186682002771
Metrics
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