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    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
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Editor-in-chief
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2024 Vol.16(2): 66-75
DOI: 10.7763/IJCTE.2024.V16.1355

Machine Learning Forecasting Model for Solar Energy Radiation

Blessing O. Abisoye1,*, Yanxia Sun1, and Zenghui Wang2,3
1. Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
2. Department of Electrical and Smart Systems Engineering, University of South Africa, Johannesburg, South Africa
3. Center for Augmented Intelligence and Data Science, University of South Africa, Johannesburg, South Africa
Email: 222244434@student.uj.ac.za (B.O.A.); ysun@uj.ac.za (Y.S.); wangz@unisa.ac.za (Z.W.)
*Corresponding author

Manuscript received August 28, 2023; revised November 2, 2023; accepted February 24, 2024; published June 26, 2024

Abstract—Renewable systems such as solar and wind are intermittent by nature. This attribute makes integrating them on a large-scale generation difficult for optimum utilization. Due to this challenge, several forecasting models have been developed to address the issue. The problems of the existing methods forecasting models are computational complexity, overfitting and low accuracy. This paper proposes a deep learning model called Long Short-Term Memory (LSTM) to forecast solar energy radiation using meteorological features. Selected hyperparameters of the proposed LSTM model are optimized with the Grid Search Cross-Validation (GridSearchCV) method. Four Machine Learning (ML) methods, Support Vector Regression (SVR), Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) regression, and stacked RF-XGBoost, are investigated as benchmark models for the proposed LSTM-GridSearchCV model. The experimentation results revealed that the proposed method is superior to the benchmark ML model regarding accuracy and performance errors technique and capable of accurately forecasting the solar energy system. It can help the practitioner make accurate decisions on integrating renewable energy into a large-scale system.

Keywords—forecasting, renewable energy, machine learning, ensemble learning, hybrid

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Cite: Blessing O. Abisoye, Yanxia Sun, and Zenghui Wang, "Machine Learning Forecasting Model for Solar Energy Radiation," International Journal of Computer Theory and Engineering, vol. 16, no. 2, pp. 66-75, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


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