Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms
Yazarlar (3)
Abdurrahman Burak Güher Osmaniye Korkut Ata University, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Prof. Dr. Bülent Yanıktepe Osmaniye Korkut Ata Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Photoenergy (Q3) | ||
| Dergi ISSN | 1110-662X Dergi Bilgileri (2020) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2020 |
| Cilt / Sayı / Sayfa | 2020 / 1 / 1–26 | DOI | 10.1155/2020/8843620 |
| Makale Linki | https://www.hindawi.com/journals/ijp/2020/8843620/ | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K-Nearest Neighbors (K-NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using … |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 16 |
| Scopus | 29 |
| Google Scholar | 40 |