Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

Sinem Uzun; Hatice Arslantaş

Makale detayları

Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

Yazarlar:

Sinem Uzun | Hatice Arslantaş

Yayıncı:

Gazi University

Ana Başlıklar:

Enerji

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.315-323

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313840

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

Yazarlar:

Sinem Uzun | Hatice Arslantaş

Yayıncı:

Gazi University

Ana Başlıklar:

Enerji

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.315-323

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313840

pISSN:

2147-9526

solar radiation

artifical neural network

latitude

Sayı

Makale detayları

Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

Yazarlar: Sinem Uzun | Hatice Arslantaş

Yayıncı: Gazi University

Ana Başlıklar: Enerji

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.315-323

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001313840

pISSN: 2147-9526

solar radiation

artifical neural network

latitude

Sayı

Yazarlar:

Sinem Uzun | Hatice Arslantaş

Yayıncı:

Gazi University

Ana Başlıklar:

Enerji

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 1, pp.315-323

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313840

pISSN:

2147-9526