Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks
Yazarlar (2)
Ali Kamil Gumar Gumar
Doç. Dr. Funda DEMİR Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | ENERGIES (Q3) | ||
| Dergi ISSN | 1996-1073 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 11-2022 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 18-11-2022 |
| Cilt / Sayı / Sayfa | 15 / 22 / 8669–8684 | DOI | 10.3390/en15228669 |
| Makale Linki | http://dx.doi.org/10.3390/en15228669 | ||
| UAK Araştırma Alanları |
Yenilenebilir Enerji Sistemleri
Yapay Zeka
Elektronik
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| Özet |
| Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony. |
| Anahtar Kelimeler |
| artificial neural network (ANN) | artificial bee colony (ABC) | genetic algorithm (GA) | particle swarm optimization (PSO) | solar photovoltaic (PV) |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 11 |