| Yazarlar |
Erdal Irmak
Türkiye |
|
Mehmet Yeşilbudak
Türkiye |
Öğr. Gör. Oğuz TAŞDEMİR
Kırşehir Ahi Evran Üniversitesi, Türkiye |
| Özet |
| Renewable energy sources play a critical role in meeting the increasing energy demand. Among them, solar energy stands out with the advantages of being environmentally friendly and protecting the ecosystem. However, its variable structure requires predicting the energy to be produced, properly. In this study, the impact of PM10 parameter on the power output prediction of photovoltaic (PV) energy plants was analyzed in a detailed manner. By the developed prediction model based on artificial neural networks (ANNs), lower root mean squared error and mean absolute percentage error were achieved. As a result, PM10 parameter has seemed to be an efficient input for the daily PV power prediction. |
| Anahtar Kelimeler |
| artificial neural networks | daily prediction | PM10 parameter | PV power |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayımlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Web of Science Kapsamındaki Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | 2023 11th International Conference on Smart Grid (icSmartGrid) |
| Kongre Tarihi | 06-06-2023 / |
| Basıldığı Ülke | Fransa |
| Basıldığı Şehir | Paris |
| Atıf Sayıları | |
| WoS | 1 |
| SCOPUS | 1 |
| Google Scholar | 1 |
