The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Short-Term Prediction of Wind Farm Power: A Data Mining Approach
Abstract
This paper examines time series models for predicting the power of a wind farm at different time scales, i.e., 10-min and hour-long intervals. The time series models are built with data mining algorithms. Five different data mining algorithms have been tested on various wind farm datasets. Two of the five algorithms performed particularly well. The support vector machine regression algorithm provides accurate predictions of wind power and wind speed at 10-min intervals up to 1 h into the future, while the multilayer perceptron algorithm is accurate in predicting power over hour-long intervals up to 4 h ahead. Wind speed can be predicted fairly accurately based on its historical values; however, the power cannot be accurately determined given a power curve model and the predicted wind speed. Test computational results of all time series models and data mining algorithms are discussed. The tests were performed on data generated at a wind farm of 100 turbines. Suggestions for future research are provided.
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Metadata
- Authors
- Kusiak, A., Zheng, H., & Song, Z.
- Venue
- IEEE Transactions on Energy Conversion
- Published
- 2009-03
- Volume
- 24
- Issue
- 1
- Pages
- 125-136
- ISSN
- 0885-8969 (ISSN-L)
- OpenAlex
- W2111307099
- Cited by
- 339
- References
- 23
- FWCI
- 19.54
- Type
- journal article
- Language
- en
- Open access
- closed
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