doi: 10.7763/IJCTE.2024.V16.1355
Machine Learning Forecasting Model for Solar Energy Radiation
Blessing O. Abisoye1,*
,
Yanxia Sun1 ,
Zenghui Wang2,3
- 1Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
- 2Department of Electrical and Smart Systems Engineering, University of South Africa, Johannesburg, South Africa
- 3Center for Augmented Intelligence and Data Science, University of South Africa, Johannesburg, South Africa
- Manuscript receivedAugust 28, 2023
- revisedNovember 2, 2023
- acceptedFebruary 24, 2024
- publishedJune 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
How to 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. https://doi.org/10.7763/IJCTE.2024.V16.1355
Copyright & License
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).