Title

Short-horizon prediction of wind power: A data-driven approach

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2010

Journal/Book/Conference Title

IEEE Transactions on Energy Conversion

Volume

25

Abstract

This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. 2006 IEEE.

Keywords

Sustainability, This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. 2006 IEEE.

Published Article/Book Citation

IEEE Transactions on Energy Conversion, 25:4 (2010) pp.1112-1122.



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URL

http://ir.uiowa.edu/cee_pubs/476