Leveraging turbine-level data for improved probabilistic wind power forecasting

Gilbert, C., Browell, J. and McMillan, D. (2020) Leveraging turbine-level data for improved probabilistic wind power forecasting. IEEE Transactions on Sustainable Energy, 11(3), pp. 1152-1160. (doi: 10.1109/TSTE.2019.2920085)

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This paper describes two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data. The first is a feature engineering approach whereby deterministic power forecasts from the turbine level are used as explanatory variables in a wind farm level forecasting model. The second is a novel bottom-up hierarchical approach where the wind farm forecast is inferred from the joint predictive distribution of the power output from individual turbines. Notably, the latter produces probabilistic forecasts that are coherent across both turbine and farm levels, which the former does not. The methods are tested at two utility scale wind farms and are shown to provide consistent improvements of up to 5%, in terms of continuous ranked probability score compared to the best performing state-of-the-art benchmark model. The bottom-up hierarchical approach provides greater improvement at the site characterized by a complex layout and terrain, while both approaches perform similarly at the second location. We show that there is a clear benefit in leveraging readily available turbine-level information for wind power forecasting.

Item Type:Articles
Additional Information:The work of C. Gilbert was supported by the University of Strathclyde’s EPSRC Centre for Doctoral Training in Wind and Marine Energy Systems under Grant EP/L016680/1. The work of J. Browell’s was supported by an EPSRC Doctoral Prize under Grant EP/M508159/1.
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Authors: Gilbert, C., Browell, J., and McMillan, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:IEEE Transactions on Sustainable Energy
ISSN (Online):1949-3037
Published Online:06 June 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in IEEE Transactions on Sustainable Energy 11(3): 1152-1160
Publisher Policy:Reproduced under a Creative Commons License

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