Reliable solar irradiance prediction using ensemble learning-based models: A comparative study
Name:
ECM-D-19-07581R2-Unmarked.pdf
Size:
634.4Kb
Format:
PDF
Description:
Accepted manuscript
Embargo End Date:
2022-02-21
Type
ArticleAuthors
Lee, Junho
Wang, Wu
Harrou, Fouzi

Sun, Ying

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
KAUST Grant Number
OSR-2019-CRG7-3800Date
2020-02-21Online Publication Date
2020-02-21Print Publication Date
2020-03Embargo End Date
2022-02-21Submitted Date
2019-11-20Permanent link to this record
http://hdl.handle.net/10754/661934
Metadata
Show full item recordAbstract
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.2020.112582Sponsors
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 BVJournal
Energy Conversion and ManagementAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S0196890420301199ae974a485f413a2113503eed53cd6c53
10.1016/j.enconman.2020.112582