Bekir YELMEN, M. Tarık ÇAKIR, H.Havva ŞAHİN, Cengiz KURT

Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi

Bu çalışma, Türkiye'nin Akdeniz bölgesi Mersin ilinde yapay sinir ağları kullanılarak seralarda salatalık(Cucumis sativus L.) yetiştiriciliğindeki enerji kullanım etkinliği analizinin belirlenmesi amacıyla yapılmıştır. Veriler 2018 yılı üretim döneminde 45 adet sera salatalık üreticisinden, yüz yüze anket yapılarak toplanmıştır. Toplam enerji tüketimi ve sera salatalık verimi sırasıyla 125612,51 MJ ha-1 ve 106600,40 kg ha-1'dir. Dizel yakıt %44,09 oranla, tüm girdiler arasında en yüksek enerji tüketimine sahiptir. Enerji endeksleri analizi, enerji oranı, enerji verimliliği, spesifik enerji, net enerji ve enerji yoğunluğunun sırasıyla yaklaşık 0,58, 0,73 kg MJ-1, 1,37 MJ kg-1, -52332,19 MJ ha-1 ve 3,22 MJ $-1 olarak elde edilmiştir. The Levenberg-Marquardt öğrenme algoritması, enerji endekslerine dayalı enerji girdilerine ve alana yönelik tahmin modellerinin hesaplanması için eğitildi. YSA modelinin sonuçları, 9-14-5 yapısının en yüksek R2 ve en düşük RMSE ve MAPE ile en iyi topolojiye ait olduğunu ortaya koydu. R2, RMSE ve MAPE oranı sırasıyla 0,933-0,991, 0,147-0,314 ve 0,011-0,021 arasında hesaplandı. Enerji kullanım etkinliği analiz sonuçlarına göre, YSA modelinin seralarda salatalık yetiştiriciliğinin enerji endekslerini yüksek doğrulukla modelleyebilmesi açısından avantajlı olduğu belirlenmiştir.

Anahtar Kelimeler:

Yapay sinir ağı, salatalık üretimi, enerji, modelleme

Modelling Energy Efficiency in Greenhouse Systems Using Artificial Neural Network (ANN)

The purpose of this study, the Mediterranean region of Turkey is made to determine the Mersin province in the neural network using the energy use efficiency in greenhouse cucumber(Cucumis sativus L.) farming in the analysis. The data were collected from 45 greenhouse cucumber producers by face-to-face questionnaire during 2018 production period. Total energy consumption and greenhouse cucumber yield are 125612,51 MJ ha-1 and 106600,40 kg ha-1, respectively. Diesel fuel, which has 44.09%, has the highest energy consumption among all inputs. Energy index analysis, energy ratio, energy efficiency, specific energy, net energy and energy intensiveness are respectively 0.58, 0.73 kg MJ-1, 1.37 MJ kg-1, -52332, 19 MJ ha-1 and 3,22 MJ$-1 respectively It was obtained as. The Levenberg-Marquardt learning algorithm has been trained to calculate energy inputs and field-based prediction models based on energy indices. The results of the ANN model revealed that the 9-14-5 structure belongs to the best topology with the highest R2 and the lowest RMSE and MAPE. The ratio of R2, RMSE and MAPE was calculated as 0.933-0.991, 0.147-0.314 and 0.011-0.021, respectively. According to the results of energy use efficiency analysis, it is determined that ANN model is advantageous in terms of cucumber cultivation in greenhouses with high accuracy modelling of energy indices.

Keywords:

Artificial neural networks, cucumber production, energy, modelling,

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