Prediction of photovoltaic panel power output using artificial neural networks learned by heuristic algorithms: A comparative study Sezgisel Algoritma Tabanli Yapay Sinir Aǧlari Kullanilarak Fotovoltaik Panel Güç Çikişlarinin Tahmini: Karşilaştirmali Bir Çalişma

DANDIL E., Gürgen E.

2nd International Conference on Computer Science and Engineering, UBMK 2017, Antalya, Türkiye, 5 - 08 Ekim 2017, ss.397-402 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/ubmk.2017.8093423
  • Basıldığı Şehir: Antalya
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.397-402
  • Anahtar Kelimeler: ANN, Back-propagation, Clonal selection algorithm, Photovoltaic panel, Power prediction, PSO
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

© 2017 IEEE.The prediction of power outputs generated from photovoltaic (PV) systems at different times is necessary for reliable and economical use of solar panels. The prediction of the power output is also very important in terms of factors such as installation of solar panels, guidance of electricity companies, energy management and distribution. In this study, we propose an Artificial Neural Network (ANN) model learned by heuristic algorithms to predict the power outputs obtained from PV panels monthly. It has been seen that ANN trained by Particle Swarm Optimization (PSO) are more successful than methods trained by the Back-Propagation(BP) and Clonal Selection Algorithm (CSA) for prediction of the power outputs obtained from PV panels placed at six different tilt angles.