Daily Prediction of PV Power Output Using Particulate Matter Parameter with Artificial Neural Networks
Yazarlar (3)
Gazi Üniversitesi, Türkiye
Gazi Üniversitesi, Türkiye
Dr. Öğr. Üyesi Oğuz TAŞDEMİR
Kırşehir Ahi Evran Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Web of Science Kapsamındaki Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/icSmartGrid58556.2023.10171103 | ||
| Kongre Adı | 2023 11th International Conference on Smart Grid (icSmartGrid) | ||
| Kongre Tarihi | 06-06-2023 / 07-06-2023 | ||
| Basıldığı Ülke | Fransa | Basıldığı Şehir | Paris |
| Bildiri Linki | 10.1109/icSmartGrid58556.2023.1017 1103. | ||
| 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 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 2 |
| SCOPUS | 2 |
| Google Scholar | 4 |