An Effective Wind Power Prediction using Latent Regression Models

Abstract
Wind power is considered one of the most promising renewable energies. Efficient prediction of wind power will support in efficiently integrating wind power in the power grid. However, the major challenge in wind power is its high fluctuation and intermittent nature, making it challenging to predict. This paper investigated and compared the performance of two commonly latent variable regression methods, namely principal component regression (PCR) and partial least squares regression (PLSR), for predicting wind power. Actual measurements recorded every 10 minutes from an actual wind turbine are used to demonstrate the prediction precision of the investigated techniques. The result showed that the prediction performances of PCR and PLSR are relatively comparable. The investigated models in this study can represent a helpful tool for model-based anomaly detection in wind turbines.

Citation
Bouyeddou, B., Harrou, F., Saidi, A., & Sun, Y. (2021). An Effective Wind Power Prediction using Latent Regression Models. 2021 International Conference on ICT for Smart Society (ICISS). doi:10.1109/iciss53185.2021.9533242

Acknowledgements
This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.

Publisher
IEEE

Conference/Event Name
2021 International Conference on ICT for Smart Society (ICISS)

DOI
10.1109/iciss53185.2021.9533242

Additional Links
https://ieeexplore.ieee.org/document/9533242/

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