Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre.
The techniques for forecasting meteorological variables are highly studied since prior knowledge of them allows for the efficient management of renewable energies, and also for other applications of science such as agriculture, health, engineering, energy, etc. In this research, the de sign, impleme...
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2023
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_version_ | 1839981020142632960 |
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author | Segovia Tapia, Jenny Aracely |
author2 | Toaquiza Camalle, Jonathan Fernando |
author2_role | author |
author_facet | Segovia Tapia, Jenny Aracely Toaquiza Camalle, Jonathan Fernando |
author_role | author |
collection | Repositorio Universidad de las Fuerzas Armadas |
dc.contributor.none.fl_str_mv | Llanos Proaño, Jacqueline del Rosario Rivas Lalaleo, David Raimundo |
dc.creator.none.fl_str_mv | Segovia Tapia, Jenny Aracely Toaquiza Camalle, Jonathan Fernando |
dc.date.none.fl_str_mv | 2023-03-16T14:48:18Z 2023-03-16T14:48:18Z 2023-02-13 |
dc.format.none.fl_str_mv | application/pdf application/pdf |
dc.identifier.none.fl_str_mv | Segovia Tapia, Jenny Aracely. Toaquiza Camalle, Jonathan Fernando (2023). Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. Carrera de Ingeniería Electrónica e Instrumentación. Universidad de las Fuerzas Armadas ESPE. Extensión Latacunga. ENI-0503 http://repositorio.espe.edu.ec/handle/21000/35763 |
dc.language.none.fl_str_mv | eng |
dc.publisher.none.fl_str_mv | Universidad de las Fuerzas Armadas ESPE. Extensión Latacunga. Carrera de Ingeniería en Electrónica e Instrumentación. |
dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
dc.source.none.fl_str_mv | reponame:Repositorio Universidad de las Fuerzas Armadas instname:Universidad de las Fuerzas Armadas instacron:ESPE |
dc.subject.none.fl_str_mv | APRENDISAJE AUTOMÁTICO MODELOS DE PRONÓSTICOS VARIABLES METEOROLÓGICAS PYTHON - LEGUAJE DE PROGRAMACIÓN |
dc.title.none.fl_str_mv | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. |
dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
description | The techniques for forecasting meteorological variables are highly studied since prior knowledge of them allows for the efficient management of renewable energies, and also for other applications of science such as agriculture, health, engineering, energy, etc. In this research, the de sign, implementation, and comparison of forecasting models for meteorological variables have been performed using different Machine Learning techniques as part of Python open-source software. The techniques implemented include multiple linear regression, polynomial regression, random forest, decision tree, XGBoost, and multilayer perceptron neural network (MLP). To identify the best technique, the mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R 2) are used as evaluation metrics. The most efficient techniques depend on the variable to be forecasting, however, it is noted that for most of them, random forest and XGBoost techniques present better performance. For temperature, the best per forming technique was Random Forest with an R 2 of 0.8631, MAE of 0.4728 °C, MAPE of 2.73%, and RMSE of 0.6621 °C; for relative humidity, was Random Forest with an R 2 of 0.8583, MAE of 2.1380RH, MAPE of 2.50 % and RMSE of 2.9003 RH; for solar radiation, was Random Forest with an R 2 of 0.7333, MAE of 65.8105 W/m2, and RMSE of 105.9141 W/m2 ; and for wind speed, was Random Forest with an R 2 of 0.3660, MAE of 0.1097 m/s, and RMSE of 0.2136 m/s. |
eu_rights_str_mv | openAccess |
format | article |
id | ESPE_b2f926440c1e9050a48634ecfc7659ce |
identifier_str_mv | Segovia Tapia, Jenny Aracely. Toaquiza Camalle, Jonathan Fernando (2023). Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. Carrera de Ingeniería Electrónica e Instrumentación. Universidad de las Fuerzas Armadas ESPE. Extensión Latacunga. ENI-0503 |
instacron_str | ESPE |
institution | ESPE |
instname_str | Universidad de las Fuerzas Armadas |
language | eng |
network_acronym_str | ESPE |
network_name_str | Repositorio Universidad de las Fuerzas Armadas |
oai_identifier_str | oai:repositorio.espe.edu.ec:21000/35763 |
publishDate | 2023 |
publisher.none.fl_str_mv | Universidad de las Fuerzas Armadas ESPE. Extensión Latacunga. Carrera de Ingeniería en Electrónica e Instrumentación. |
reponame_str | Repositorio Universidad de las Fuerzas Armadas |
repository.mail.fl_str_mv | . |
repository.name.fl_str_mv | Repositorio Universidad de las Fuerzas Armadas - Universidad de las Fuerzas Armadas |
repository_id_str | 2042 |
spelling | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre.Segovia Tapia, Jenny AracelyToaquiza Camalle, Jonathan FernandoAPRENDISAJE AUTOMÁTICOMODELOS DE PRONÓSTICOSVARIABLES METEOROLÓGICASPYTHON - LEGUAJE DE PROGRAMACIÓNThe techniques for forecasting meteorological variables are highly studied since prior knowledge of them allows for the efficient management of renewable energies, and also for other applications of science such as agriculture, health, engineering, energy, etc. In this research, the de sign, implementation, and comparison of forecasting models for meteorological variables have been performed using different Machine Learning techniques as part of Python open-source software. The techniques implemented include multiple linear regression, polynomial regression, random forest, decision tree, XGBoost, and multilayer perceptron neural network (MLP). To identify the best technique, the mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R 2) are used as evaluation metrics. The most efficient techniques depend on the variable to be forecasting, however, it is noted that for most of them, random forest and XGBoost techniques present better performance. For temperature, the best per forming technique was Random Forest with an R 2 of 0.8631, MAE of 0.4728 °C, MAPE of 2.73%, and RMSE of 0.6621 °C; for relative humidity, was Random Forest with an R 2 of 0.8583, MAE of 2.1380RH, MAPE of 2.50 % and RMSE of 2.9003 RH; for solar radiation, was Random Forest with an R 2 of 0.7333, MAE of 65.8105 W/m2, and RMSE of 105.9141 W/m2 ; and for wind speed, was Random Forest with an R 2 of 0.3660, MAE of 0.1097 m/s, and RMSE of 0.2136 m/s.ESPE-LUniversidad de las Fuerzas Armadas ESPE. Extensión Latacunga. Carrera de Ingeniería en Electrónica e Instrumentación.Llanos Proaño, Jacqueline del RosarioRivas Lalaleo, David Raimundo2023-03-16T14:48:18Z2023-03-16T14:48:18Z2023-02-13info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/pdfSegovia Tapia, Jenny Aracely. Toaquiza Camalle, Jonathan Fernando (2023). Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. Carrera de Ingeniería Electrónica e Instrumentación. Universidad de las Fuerzas Armadas ESPE. Extensión Latacunga.ENI-0503http://repositorio.espe.edu.ec/handle/21000/35763enginfo:eu-repo/semantics/openAccessreponame:Repositorio Universidad de las Fuerzas Armadasinstname:Universidad de las Fuerzas Armadasinstacron:ESPE2024-07-27T10:37:24Zoai:repositorio.espe.edu.ec:21000/35763Institucionalhttps://repositorio.espe.edu.ec/Universidad públicahttps://www.espe.edu.ec/https://repositorio.espe.edu.ec/oai.Ecuador...opendoar:20422024-07-27T10:37:24Repositorio Universidad de las Fuerzas Armadas - Universidad de las Fuerzas Armadasfalse |
spellingShingle | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. Segovia Tapia, Jenny Aracely APRENDISAJE AUTOMÁTICO MODELOS DE PRONÓSTICOS VARIABLES METEOROLÓGICAS PYTHON - LEGUAJE DE PROGRAMACIÓN |
status_str | publishedVersion |
title | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. |
title_full | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. |
title_fullStr | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. |
title_full_unstemmed | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. |
title_short | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. |
title_sort | Sistema de predicción de variables meteorológicas utilizando Machine Learning y Software Libre. |
topic | APRENDISAJE AUTOMÁTICO MODELOS DE PRONÓSTICOS VARIABLES METEOROLÓGICAS PYTHON - LEGUAJE DE PROGRAMACIÓN |
url | http://repositorio.espe.edu.ec/handle/21000/35763 |