Enhanced PV Power Prediction Considering PM10 Parameter by Hybrid JAYA-ANN Model
Yazarlar (3)
Dr. Öğr. Üyesi Oğuz TAŞDEMİR Kırşehir Ahi Evran Üniversitesi, Türkiye
Erdal Irmak Gazi Üniversitesi, Türkiye
Mehmet Yeşilbudak Nevşehir Hacı Bektaş Veli Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Electric Power Components and Systems (Q3) | ||
| Dergi ISSN | 1532-5008 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 03-2024 |
| Cilt / Sayı / Sayfa | 52 / 11 / 1998–2007 | DOI | 10.1080/15325008.2024.2322668 |
| Makale Linki | https://doi.org/10.1080/15325008.2024.2322668 | ||
| Özet |
| The demand for electrical energy is continuously increasing in these days, particularly due to advancements in the industrial sector. This surge in demand has underscored the importance of seeking alternative energy sources, with solar energy emerging as a standout option due to its low investment costs and environmental friendliness. However, the variability in photovoltaic power production, influenced by meteorological data, necessitates accurate prediction methods. To enhance the precision of these predictions, incorporating new parameters alongside existing meteorological data is advantageous. In this regard, this study explores the impact of the particulate matter (PM10) parameter on photovoltaic power prediction using artificial neural network (ANN) model and JAYA-ANN. Comparing the prediction results based on root mean squared and mean absolute percentage errors reveals that the hybrid JAYA … |
| Anahtar Kelimeler |
| artificial neural network | comparison | metaheuristic optimization | Photovoltaic power production | prediction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 10 |
| Scopus | 5 |
| Web of Science | 5 |