An Effective Wind Power Prediction using Latent Regression Models
KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Environmental Statistics Group
KAUST Grant NumberOSR-2019-CRG7-3800
Permanent link to this recordhttp://hdl.handle.net/10754/671235
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AbstractWind 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.
CitationBouyeddou, 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
SponsorsThis 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.
Conference/Event name2021 International Conference on ICT for Smart Society (ICISS)