Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software
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 (...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00051.pdf |
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| author | Tucumbi Lisbeth Guano Jefferson Salazar-Achig Roberto Jiménez J. Diego L. |
| author_facet | Tucumbi Lisbeth Guano Jefferson Salazar-Achig Roberto Jiménez J. Diego L. |
| author_sort | Tucumbi Lisbeth |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f6ecd37721d34f408f3b42ccf039aa68 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-f6ecd37721d34f408f3b42ccf039aa682025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010005110.1051/e3sconf/202560100051e3sconf_icegc2024_00051Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source SoftwareTucumbi Lisbeth0Guano Jefferson1Salazar-Achig Roberto2Jiménez J. Diego L.3Facultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de CotopaxiFacultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de CotopaxiFacultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de CotopaxiFacultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de CotopaxiThe 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00051.pdfdecision treemachine learningrandom forestsolar radiationpredictionpython |
| spellingShingle | Tucumbi Lisbeth Guano Jefferson Salazar-Achig Roberto Jiménez J. Diego L. Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software E3S Web of Conferences decision tree machine learning random forest solar radiation prediction python |
| title | Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software |
| title_full | Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software |
| title_fullStr | Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software |
| title_full_unstemmed | Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software |
| title_short | Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software |
| title_sort | solar radiation prediction using decision tree and random forest models in open source software |
| topic | decision tree machine learning random forest solar radiation prediction python |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00051.pdf |
| work_keys_str_mv | AT tucumbilisbeth solarradiationpredictionusingdecisiontreeandrandomforestmodelsinopensourcesoftware AT guanojefferson solarradiationpredictionusingdecisiontreeandrandomforestmodelsinopensourcesoftware AT salazarachigroberto solarradiationpredictionusingdecisiontreeandrandomforestmodelsinopensourcesoftware AT jimenezjdiegol solarradiationpredictionusingdecisiontreeandrandomforestmodelsinopensourcesoftware |