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    Reliable solar irradiance prediction using ensemble learning-based models: A comparative study

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    Name:
    ECM-D-19-07581R2-Unmarked.pdf
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    634.4Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2022-02-21
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    Type
    Article
    Authors
    Lee, Junho cc
    Wang, Wu
    Harrou, Fouzi cc
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    KAUST Grant Number
    OSR-2019-CRG7-3800
    Date
    2020-02-21
    Online Publication Date
    2020-02-21
    Print Publication Date
    2020-03
    Embargo End Date
    2022-02-21
    Submitted Date
    2019-11-20
    Permanent link to this record
    http://hdl.handle.net/10754/661934
    
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    Abstract
    Accurately 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.
    Citation
    Lee, 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
    Sponsors
    The 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.
    Publisher
    Elsevier BV
    Journal
    Energy Conversion and Management
    DOI
    10.1016/j.enconman.2020.112582
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0196890420301199
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.enconman.2020.112582
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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