Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example
This research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiatio...
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| Format: | Article |
| Language: | English |
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Gazi University
2024-03-01
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| Series: | Gazi Üniversitesi Fen Bilimleri Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/pub/gujsc/issue/83732/1420617 |
| _version_ | 1797205472905789440 |
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| author | Sinem Uzun Hatice Arslantaş |
| author_facet | Sinem Uzun Hatice Arslantaş |
| author_sort | Sinem Uzun |
| collection | DOAJ |
| description | This research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiation values, despite their geographical differences. In this study, solar radiation prediction was made for 3 cities with a single-layer neural network. Monthly solar radiation intensity was estimated for the 10-year period between 2012 and 2022 with a total of 4764 samples taken from the General Directorate of State Meteorology. An artificial neural network model was developed with 8 units in the first hidden layer and 4 units in the second hidden layer. The optimizer used in compiling the model was determined as Adam, the loss function as 'mean_squared_error' and the metric as 'mse'. ReLU activation function was used in the input layer and hidden layers. A 10-year solar radiation intensity value was used in the output layer. 70% of the data set is reserved for training and 30% for testing data set. As a result, similar solar radiation trends were obtained in the same latitude regions, the results were confirmed by meteorological data. |
| first_indexed | 2024-04-24T08:51:40Z |
| format | Article |
| id | doaj.art-1511b4d8a46d45aebac80284e848ea38 |
| institution | Directory Open Access Journal |
| issn | 2147-9526 |
| language | English |
| last_indexed | 2024-04-24T08:51:40Z |
| publishDate | 2024-03-01 |
| publisher | Gazi University |
| record_format | Article |
| series | Gazi Üniversitesi Fen Bilimleri Dergisi |
| spelling | doaj.art-1511b4d8a46d45aebac80284e848ea382024-04-16T09:19:39ZengGazi UniversityGazi Üniversitesi Fen Bilimleri Dergisi2147-95262024-03-01121315323https://doi.org/10.29109/gujsc.1420617Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum exampleSinem Uzun0https://orcid.org/0000-0002-2814-1062 Hatice Arslantaş1https://orcid.org/0000-0003-1060-0707ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİThis research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiation values, despite their geographical differences. In this study, solar radiation prediction was made for 3 cities with a single-layer neural network. Monthly solar radiation intensity was estimated for the 10-year period between 2012 and 2022 with a total of 4764 samples taken from the General Directorate of State Meteorology. An artificial neural network model was developed with 8 units in the first hidden layer and 4 units in the second hidden layer. The optimizer used in compiling the model was determined as Adam, the loss function as 'mean_squared_error' and the metric as 'mse'. ReLU activation function was used in the input layer and hidden layers. A 10-year solar radiation intensity value was used in the output layer. 70% of the data set is reserved for training and 30% for testing data set. As a result, similar solar radiation trends were obtained in the same latitude regions, the results were confirmed by meteorological data.https://dergipark.org.tr/tr/pub/gujsc/issue/83732/1420617solar radiationartifical neural networklatitude |
| spellingShingle | Sinem Uzun Hatice Arslantaş Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example Gazi Üniversitesi Fen Bilimleri Dergisi solar radiation artifical neural network latitude |
| title | Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example |
| title_full | Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example |
| title_fullStr | Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example |
| title_full_unstemmed | Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example |
| title_short | Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example |
| title_sort | determination of radiation value by month using artificial neural network model ankara sivas erzurum example |
| topic | solar radiation artifical neural network latitude |
| url | https://dergipark.org.tr/tr/pub/gujsc/issue/83732/1420617 |
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