Unidades Recurrentes Cerradas (GRU) vs Redes Neuronales Artificiales en la predicción de la generación Eléctrica de la Central Hidroeléctrica Illuchi.
The prediction of events has been, since ancient times, a phenomenon capable of generating curiosity in human beings, however, to achieve a projection of a future event, a detailed analysis of data is required to predict subsequent events, with this idea. Objectives: The objective of the research wa...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Ecuador |
| Institución: | Universidad Técnica de Cotopaxi |
| Repositorio: | Repositorio Universidad Técnica de Cotopaxi |
| Idioma: | español |
| OAI Identifier: | oai:oai:repositorio.utc.edu.ec:27000:27000/10774 |
| Acceso en línea: | http://repositorio.utc.edu.ec/handle/27000/10774 |
| Access Level: | acceso abierto |
| Palabra clave: | ADAM PYTHON PREDICCIÓN REDES NEURONALES ARTIFICIALES ELÉCTRICA |
| Sumario: | The prediction of events has been, since ancient times, a phenomenon capable of generating curiosity in human beings, however, to achieve a projection of a future event, a detailed analysis of data is required to predict subsequent events, with this idea. Objectives: The objective of the research was to develop two prediction systems by applying artificial neural networks and GRU to determine the predicted electricity generation at the ILLUCHI HYDROELECTRIC PLANT. Methodology: The data used for this study was collected from ELEPCO S.A. operators. based on the years 2008 - 2020. The input variables were the date and the energy generated to develop different cases with different conditions in order to reach a possible successful Recurrent Neural Network model. Results: Once the model variables were understood, the data were divided into two groups: training 70% and validation 30% respectively. For the corresponding training, the ADAM algorithm and the libraries provided by Python were used. |
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