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dc.contributor.authorBouyeddou, Benamar
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSaidi, Ahmed
dc.contributor.authorSun, Ying
dc.date.accessioned2021-09-15T07:28:12Z
dc.date.available2021-09-15T07:28:12Z
dc.date.issued2021-08-02
dc.identifier.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
dc.identifier.doi10.1109/iciss53185.2021.9533242
dc.identifier.urihttp://hdl.handle.net/10754/671235
dc.description.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.
dc.description.sponsorshipThis 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.
dc.publisherIEEE
dc.relation.urlhttps://ieeexplore.ieee.org/document/9533242/
dc.rightsArchived with thanks to IEEE
dc.titleAn Effective Wind Power Prediction using Latent Regression Models
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.conference.date2-4 Aug. 2021
dc.conference.name2021 International Conference on ICT for Smart Society (ICISS)
dc.conference.locationBandung, Indonesia
dc.eprint.versionPost-print
dc.contributor.institutionAbou Bekr Belkaid University, STIC Lab., Department of Telecommunications, Tlemcen, Algeria
dc.contributor.institutionUniversity of Saida-Dr Moulay Tahar, Department of Electronics, Faculty of Technology, Saida, Algeria
dc.contributor.institutionAhmed Draia University, Department of Science and Technology, Laboratory of Sustainable Development and Computer Science, Adrar 01000, Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2019-CRG7-3800
refterms.dateFOA2021-09-15T07:29:30Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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