Modeling a Solar Power Plant with Artificial Neural Networks

Yıl 2023, , 201 - 206, 31.12.2023

Seren Arslan , Hikmet Esen , Engin Avcı , Can Cengiz

https://doi.org/10.46460/ijiea.1336917

Öz

This study will enable us to estimate the power of the solar power plant with measurement data such as outdoor temperature-humidity, wind and precipitation amount, to protect the system from imbalance, and to determine the instant and daily effective energy trade more easily. By taking the data from the solar power plant installed in Samsun, Turkey, estimation was made with Artificial Neural Networks for electricity generation. In this study, Levenberg-Marguardt feed-forward backprop learning algorithm was used to find the best approach in the network. The best prediction results were obtained from the 2-layer and 5-neuron Artificial Neural Networks model, and it was observed that the system gave better training results as the number of iterations increased (multiple determination coefficient, R2, 0.99818).

Anahtar Kelimeler

Artificial neural network, Solar, Energy, Power plant, Electricity generation

Yapay Sinir Ağları ile Güneş Enerjisi Santralinin Modellenmesi

Öz

Bu çalışma, dış ortam sıcaklığı-nemi, rüzgar ve yağış miktarı gibi ölçüm verileriyle güneş enerjisi santralinin gücünü tahmin etmemizi, sistemi dengesizlikten korumamızı, anlık ve günlük efektif enerji ticaretini daha kolay belirlememizi sağlayacaktır. Samsun'da kurulu güneş enerjisi santralinden veriler alınarak elektrik üretimi için Yapay Sinir Ağları ile tahmin yapıldı. Bu çalışmada ağdaki en iyi yaklaşımı bulmak için Levenberg-Marguardt ileri beslemeli backprop öğrenme algoritması kullanılmıştır. En iyi tahmin sonuçları 2 katmanlı ve 5 nöronlu Yapay Sinir Ağları modelinden elde edilmiş olup, yineleme sayısı arttıkça sis-temin daha iyi eğitim sonuçları verdiği gözlemlenmiştir (çoklu belirleme katsayısı, R2, 0,99818).

Anahtar Kelimeler

Yapay Sinir Ağı, Güneş, Enerji, Enerji Santrali, Elektrik Üretimi

Kaynakça

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