An Effective Wind Power Prediction using Latent Regression Models

Benamar Bouyeddou, Fouzi Harrou, Ahmed Saidi, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2021 International Conference on ICT for Smart Society (ICISS)
StatePublished - Aug 2 2021


Dive into the research topics of 'An Effective Wind Power Prediction using Latent Regression Models'. Together they form a unique fingerprint.

Cite this