Issue |
E3S Web Conf.
Volume 601, 2025
The 3rd International Conference on Energy and Green Computing (ICEGC’2024)
|
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Article Number | 00051 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/e3sconf/202560100051 | |
Published online | 16 January 2025 |
Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software
Facultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ecuador
* Corresponding author: edgar.salazar7619@utc.edu.ec
The present research focuses on solar radiation prediction, which is important for energy production in thermal and solar systems. For this purpose, open-source software (Python) and a methodology involving the creation, implementation, and testing of specific machine learning models random forest (RF) and decision tree (DT) were used. The metrics used to identify the effectiveness of the models in predicting solar radiation were the coefficient (R2), the mean square error (MSE), and the mean absolute error (MAE). The evaluation of the two methods is presented in three cases: for one, two, and seven days. The results show that the RF model has better results than the DT, with MAE and MSE values of 36.96 and 4238.77, respectively, and a determination coefficient of 0.96. The study emphasizes the importance of selecting the appropriate model based on the prediction horizon to estimate solar availability and improve solar and thermal energy system planning.
Key words: Decision tree / machine learning / random forest / solar radiation / prediction / Python
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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