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Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy

Permanent lenke
https://hdl.handle.net/10037/26391
DOI
https://doi.org/10.1016/j.egyr.2022.08.105
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Dato
2022-08-22
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Chen, Hao; Zhang, Qixia; Birkelund, Yngve
Sammendrag
The quality of wind data from the numerical weather prediction significantly influences the accuracy of wind power forecasting systems for wind parks. Therefore, an in-depth investigation of these wind data themselves is essential to improve wind power generation efficiency and maintain grid reliability. This paper proposes a novel framework based on machine learning for concurrently analyzing and forecasting predictive errors, called residuals, of wind speed and direction from a numerical weather prediction model versus measurements over a while. The performance of the framework is testified by a wind farm inside the Arctic. It is demonstrated that the residuals still contain significant meteorological information and can be effectively predicted with machine learning and the linear autoregression works well for multi-timesteps predictions of overall, East-West, East–West,​ and North-South North–South wind speeds residuals by comparing the four forecast learning algorithms’ performance. The predictions may be applied to correct the NWP wind model, making quality feedback improvements for inputs for wind power forecasting systems. 
Forlag
Elsevier
Sitering
Chen, Zhang, Birkelund. Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy. Energy Reports. 2022
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  • Artikler, rapporter og annet (teknologi og sikkerhet) [361]
Copyright 2022 The Author(s)

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