Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
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.
Açıklama
Anahtar Kelimeler
artificial neural network (ANN), artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO), solar photovoltaic (PV)
Kaynak
Energies
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
15
Sayı
22
