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650.#.4.x: Físico Matemáticas y Ciencias de la Tierra

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336.#.#.a: Artículo

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351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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270.1.#.d: México

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883.#.#.a: Revistas UNAM

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856.4.0.u: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/53131/46889

100.1.#.a: Comert, Mehmet Murat; Adem, Kemal; Erdogan, Muberra

524.#.#.a: Comert, Mehmet Murat, et al. (2023). Comparative analysis of estimated solar radiation with different learning methods and empirical models. Atmósfera; Vol. 37, 2023; 273-284. Recuperado de https://repositorio.unam.mx/contenidos/4128794

245.1.0.a: Comparative analysis of estimated solar radiation with different learning methods and empirical models

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-03-07

653.#.#.a: Solar radiation; Empirical Equations; Machine learning; Deep learning; LSTM model

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC 4.0 Internacional, https://creativecommons.org/licenses/by-nc/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editora@atmosfera.unam.mx

884.#.#.k: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/53131

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041.#.7.h: eng

520.3.#.a: Solar radiation, which is used in hydrological and agricultural modeling, agricultural, solar energy systems, and climatological studies, is the most important element of the energy reaching the earth. The present study compared the performance of two empirical equations -Angstrom and Hargreaves-Samani equations- and three machine learning models -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-. Various learning models were developed for the variables used in each empirical equation. In the present study, monthly data of six stations in Turkey, three stations receiving the most solar radiation and three stations receiving the lowest solar radiation, were used. In terms of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and determination coefficient (R2) values of each model, the LSTM was the most successful model, followed by ANN and SVM. The MAE value was 2.65 with the Hargreaves-Samani equation and decreased to 0.987 with the LSTM model, while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model. The study revealed that the deep learning model is more appropriate to use than the empirical equations, even in cases with limited data.

773.1.#.t: Atmósfera; Vol. 37 (2023); 273-284

773.1.#.o: https://www.revistascca.unam.mx/atm/index.php/atm/index

022.#.#.a: ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

310.#.#.a: Trimestral

300.#.#.a: Páginas: 273-284

264.#.1.b: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

doi: https://doi.org/10.20937/ATM.53131

harvesting_date: 2023-06-20 16:00:00.0

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Artículo

Comparative analysis of estimated solar radiation with different learning methods and empirical models

Comert, Mehmet Murat; Adem, Kemal; Erdogan, Muberra

Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM, publicado en Atmósfera, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
Revista
Repositorio
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Comert, Mehmet Murat, et al. (2023). Comparative analysis of estimated solar radiation with different learning methods and empirical models. Atmósfera; Vol. 37, 2023; 273-284. Recuperado de https://repositorio.unam.mx/contenidos/4128794

Descripción del recurso

Autor(es)
Comert, Mehmet Murat; Adem, Kemal; Erdogan, Muberra
Tipo
Artículo de Investigación
Área del conocimiento
Físico Matemáticas y Ciencias de la Tierra
Título
Comparative analysis of estimated solar radiation with different learning methods and empirical models
Fecha
2023-03-07
Resumen
Solar radiation, which is used in hydrological and agricultural modeling, agricultural, solar energy systems, and climatological studies, is the most important element of the energy reaching the earth. The present study compared the performance of two empirical equations -Angstrom and Hargreaves-Samani equations- and three machine learning models -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-. Various learning models were developed for the variables used in each empirical equation. In the present study, monthly data of six stations in Turkey, three stations receiving the most solar radiation and three stations receiving the lowest solar radiation, were used. In terms of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and determination coefficient (R2) values of each model, the LSTM was the most successful model, followed by ANN and SVM. The MAE value was 2.65 with the Hargreaves-Samani equation and decreased to 0.987 with the LSTM model, while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model. The study revealed that the deep learning model is more appropriate to use than the empirical equations, even in cases with limited data.
Tema
Solar radiation; Empirical Equations; Machine learning; Deep learning; LSTM model
Idioma
eng
ISSN
ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

Enlaces