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dc.contributor.authorLee, Junho
dc.contributor.authorWang, Wu
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.date.accessioned2020-03-08T13:01:37Z
dc.date.available2020-03-08T13:01:37Z
dc.date.issued2020-02-21
dc.date.submitted2019-11-20
dc.identifier.citationLee, J., Wang, W., Harrou, F., & Sun, Y. (2020). Reliable solar irradiance prediction using ensemble learning-based models: A comparative study. Energy Conversion and Management, 208, 112582. doi:10.1016/j.enconman.2020.112582
dc.identifier.doi10.1016/j.enconman.2020.112582
dc.identifier.urihttp://hdl.handle.net/10754/661934
dc.description.abstractAccurately predicting solar irradiance is important in designing and efficiently managing photovoltaic systems. This paper aims to provide a reliable short-term prediction of solar irradiance based on various meteorological factors using ensemble learning-based models that take into account the time-dependent nature of the solar irradiance data. The use of ensemble learning models is motivated by their desirable characteristics in combining several weak regressors to achieve an improved prediction quality relative to conventional single learners. Furthermore, they reduce the overall prediction error and have the ability to combine different models. In this paper, we first investigate the prediction performance of the well-known ensemble methods, Boosted Trees, Bagged Trees, Random Forest, and Generalized Random Forest in short-term prediction of solar irradiance. The performance of these ensemble methods has been compared to two commonly known prediction methods namely Gaussian process regression, and Support Vector Regression. Typical Meteorological Year data are used to verify the prediction performance of the considered models. Results showed that ensemble methods offer superior prediction performance compared to the individual regressors. Furthermore, the results showed that the ensemble models have a consistent and reliable prediction when applied to data from different locations. Lastly, variables contribution assessment showed that the lagged solar irradiance variables contribute significantly to the ensemble models, which help in designing more parsimonious models.
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0196890420301199
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Energy Conversion and Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy Conversion and Management, [[Volume], [Issue], (2020-02-21)] DOI: 10.1016/j.enconman.2020.112582 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleReliable solar irradiance prediction using ensemble learning-based models: A comparative study
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.identifier.journalEnergy Conversion and Management
dc.rights.embargodate2022-02-21
dc.eprint.versionPost-print
kaust.personLee, Junho
kaust.personWang, Wu
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2019-CRG7-3800
dc.date.accepted2020-02-05
refterms.dateFOA2020-03-09T07:15:43Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)
dc.date.published-online2020-02-21
dc.date.published-print2020-03


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