A comparison of machine learning algorithms for forecasting solar irradiance in Eskişehir, Turkey

Ozan AYKO; Sinem BOZKURT KESER

doi:https://doi.org/10.18100/ijamec.995506

Makale detayları

A comparison of machine learning algorithms for forecasting solar irradiance in Eskişehir, Turkey

Yazarlar:

Ozan AYKO | Sinem BOZKURT KESER

Yayıncı:

Selcuk University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Applied Mathematics Electronics and Computers 2021 Vol.9 Issue 4, pp.103-109

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000475671

Sıklık:

Üç Aylık

eISSN:

2147-8228

pISSN:

2147-8228

DOI:

https://doi.org/10.18100/ijamec.995506

Özet:

 

This work compares the efficiency of 45 different machine learning (ML) algorithms to provide a comprehensive and most accurate model for global horizontal solar irradiance (GHSI) prediction in Eskişehir, Turkey. The dataset is provided by NASA Prediction of Worldwide Energy Resource (POWER) as satellite data that involves some characteristic weather condition variables such as temperature, precipitation, humidity etc. over 35 years. Some ML algorithms such as Extra Trees, LightGBM, HistGB, Random Forest (RF), Bagging and Decision Tree exhibit better performance among the others with commonly used statistical evaluation metrics in literature such as coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). In addition, Extra Tress regression slightly outperformed the rest of ensemble learning methods with R² of 0.99, RMSE of 8.05, MAE of 5.67, MAPE of 4%. Finally, the outcome demonstrates that the ML algorithms belonging to ensemble learning family achieved great results in GHSI prediction at specific location.

Makale detayları

A comparison of machine learning algorithms for forecasting solar irradiance in Eskişehir, Turkey

Yazarlar: Ozan AYKO | Sinem BOZKURT KESER

Yayıncı: Selcuk University

Ana Başlıklar: Uygulamalı fizik

Dergi: International Journal of Applied Mathematics Electronics and Computers 2021 Vol.9 Issue 4, pp.103-109

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000475671

Sıklık: Üç Aylık

eISSN: 2147-8228

pISSN: 2147-8228

DOI: https://doi.org/10.18100/ijamec.995506

Sayı

Yazarlar:

Ozan AYKO | Sinem BOZKURT KESER

Yayıncı:

Selcuk University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Applied Mathematics Electronics and Computers 2021 Vol.9 Issue 4, pp.103-109

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000475671

Sıklık:

Üç Aylık

eISSN:

2147-8228

pISSN:

2147-8228

DOI:

https://doi.org/10.18100/ijamec.995506