Predicción de generación en una mini central hidroeléctrica usando aprendizaje automático y software libre
| dc.contributor.advisor | Salazar Achig, Edgar Roberto | |
| dc.contributor.author | Ulloa Chipantiza, Lenin Alejandro | |
| dc.contributor.author | Pilataxi Molina, Freddy Wladimir | |
| dc.date.accessioned | 2026-04-10T21:17:59Z | |
| dc.date.available | 2026-04-10T21:17:59Z | |
| dc.date.issued | 2025-10-01 | |
| dc.description.abstract | Introduction: Ecuadorian electric companies that own run-of-the-river hydroelectric plants with regulating reservoirs equal to or less than one day must submit the hourly generation curve planned for the following day to the National Electricity Operator of Ecuador (CENACE) before 10:00 am daily, in addition to having long-term estimates that allow for optimizing their operational planning. Objective: To predict the behavior of electrical power generation at the Illuchi 1 run-of-river plant by applying machine learning methods, and then determine the most efficient method for each time scenario. Method: For the development of this study, a historical database of electrical power generation from the Illuchi 1 mini hydroelectric plant was compiled, corresponding to a period of 3 years, 7 months, which was ordered chronologically and subsequently preprocessed. The open-source software Python was used, applying a methodology based on the model and evaluation of machine learning techniques such as Linear Regression, LSTM, GRU and XGBoost. Results: The XGBoost algorithm showed better prediction performance for one and seven days, obtaining mean absolute error MAE values of 39.26 [W] and 25.60 [W] respectively and the coefficient of determination R2 of 0.44 and 0.79. On the other hand, the GRU model showed greater prediction accuracy in the two-day horizon, reaching a mean absolute error MAE of 36.03 [W] and its coefficient of determination R2 of 0.61. Conclusions: XGBoost and GRU stand out from other prediction methods due to their ability to identify non-linear models, in order to optimize their forecast accuracy at different time intervals. | |
| dc.format.extent | 11 Páginas | |
| dc.identifier.citation | Ulloa-Chipantiza L, Pilataxi-Molina F, Salazar-Achig R, Jiménez J DL. Generation Prediction in a Mini Hydroelectric Power Plant Using Machine Learning and Open-Source Software. Salud, Ciencia y Tecnología [Internet]. 2025 Oct. 1 [cited 2026 Apr. 10];5:2244. Available from: https://sct.ageditor.ar/index.php/sct/article/view/2244 | |
| dc.identifier.other | UTC-CIYA-ELE-2026-008-ART | |
| dc.identifier.uri | https://sct.ageditor.ar/index.php/sct/article/view/2244 | |
| dc.identifier.uri | https://doi.org/10.56294/saludcyt20252244 | |
| dc.identifier.uri | https://repositorio.utc.edu.ec/handle/123456789/15489 | |
| dc.language.iso | en | |
| dc.publisher | Ecuador: Latacunga: Universidad Técnica de Cotopaxi: (UTC) | |
| dc.subject | MACHINE LEARNING | |
| dc.subject | GENERATION | |
| dc.subject | PREDICTION | |
| dc.title | Predicción de generación en una mini central hidroeléctrica usando aprendizaje automático y software libre | |
| dc.type | Article |