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

Yıl 2021, Cilt: 24 Sayı: 1, 151 - 160, 01.03.2021
https://doi.org/10.2339/politeknik.680921

Öz

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.

Kaynakça

  • Kurtar E.S., Balkaya A., Göçmen M., Karaağaç O., “Hıyara (Cucumis sativus L.) anaç olabilecek kabak (Cucurbita spp.) genotiplerinde ışınlanmış polen tekniği ile dihaploidizasyon”,Selçuk Tarım ve Gıda Bilimleri Dergisi, 31 (1), 34-41, (2017).
  • Li X.Z., Chen S.X., “Screening and Identification of Cucumber Germplasm and Rootstock Resistance against the Root-Knot Nematode (Meloidogyne incognita)”, Genetics and Molecular Research, 16 (2), gmr16029383, (2017).
  • Anonymous, 2017a., “http://www.fao.org/faostat/en/#data/QC” (Erişim tarihi: 25.04.2017)
  • Pishgar-Komleh S.H., Omid M., Heidari M.D., “On the study of energy use and GHG (greenhouse gas) emissions in greenhouse cucumber production in Yazd province”. Energy, 59: 63–71, (2013).
  • Taki M., Yildizhan H., “ Evaluation the sustainable energy applications for fruit and vegetable productions processes; case study”, Greenhouse cucumber production. Journal of Cleaner Production, 199: 164–172, (2018).
  • Rohani A., Taki M., Abdollahpour M.A.. “Novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)”, Renewable Energy, 115 :411–422, 2018.
  • Anonymous, “Batı Akdeniz Kalkınma Ajansı (Antalya-Isparta-Burdur) Antalya’da Tarım Sektörünün Sorunları Ve Çözüm Önerileri Çalıştayı”, Örtüaltı Sebzecilik Alt Sektörü, Çalışma Grubu Raporu, (2010).
  • Sevgican A.Y., Tüzel, A., Gül, R.Z., “Türkiye'de Örtüaltı Yetistiriciligi Türkiye Ziraat Mühendisligi V. Teknik Kongresi”, Cilt: 2, 679-707, (2000).
  • Anonymous, 2017b., http://bahcebitkileri.cu.edu.tr/upload/nturemis/turkiyeortualti.pdf (Erişim tarihi: 15.09.2017).
  • Öztemel E., “Yapay Sinir ağları”, Papatya Yayınları 3. Baskı, (2012).
  • Safa M., Samarasinghe S., “Determination and modelling of energy consumption in wheat production using neural networks: A case study in canterbury province, Newzealand”, Energy, 36: 5140- 5147,(2011).
  • Haykin S., “Neural Networks, A Comprehensive Foundation”, Macmillan College Publishing Company, Inc., New Jersey. , (1994).
  • Özcalik H.R., Kucuktufekci A., “Dinamik Sistemlerin Yapay Sinir Ağları ile Düz ve Ters Modellenmesi”, KSÜ Fen ve Mühendislik Dergisi,6(1), 26-35, (2003).
  • Yazdani M., Saghafian B., Mahdian M., Soltani S., “Monthly runoff estimation using artificial neural Networks”, Journal of Agricultural Science and Technology, 11: 335–362, (2009).
  • Farjam A., Omid M., Akaram A., Fazel Niari Z., “A neural network based modeling and sensitivity analysis of energy inputs for predicting seed and grain corn yields”, Journal of Agricultural Science and Technology, 16: 767–778, (2014).
  • Gezer I., Acaroglu M., Haciseferogullari H., “Use of energy and labor in apricot agriculture in Turkey”, Bioenergy ,24 (3), 215-219, (2003).
  • Mohammadi A., Rafiee S., Mohtasebi S.S., Mousavi-Avval S.H., Rafiee H., “Energy inputs – yield relationship and cost analysis of kiwifruit production in Iran”, Renew. Energy, 35, 1071¬1075, (2010).
  • Rahman M.M., Bala B.K., “Modelling of jute production using artificial neural networks”. Biosys. Eng., 105 (3), 350-356, (2010).
  • Tabatabaie S.M.H., Rafiee S., Keyhani A., Ebrahimi A., “Energy and economic assessment of prune production in Tehran province of Iran”, J. Clean. Prod., 39, 280-284, (2013).
  • Khoshnevisan B., Rafiee B., Omid M., Mousazadeh H., “Prognostication of environmental indices in potato production using artificial neural networks”, J. Clean. Prod. 52, 402-409, (2013a.).
  • Ozkan B., Fert C., Karadeniz C.F., “Energy and cost analysis for greenhouse and open-field grape Production”, Energy, 32: 1500–1504, (2007).
  • Zangeneh M., Omid M., Akram A., “Assessment of machinery energy ratio in potato production by means of artificial neural network”, African Journal of Agricultural Research, 5: 993–998, (2010).
  • Houshyar E., Sheikh Davoodi M., Bahrami H., Kiani S., Houshyar M., “Energy use forecasting for wheat production utilizing artificial neural network”, Word Applied Science Journal, 10: 958–962, (2010).
  • Zangeneh M., Omid M., Akram A., “A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran”, Spanish Journal of Agricultural Research, 3: 661–671, (2011).
  • Hamedani S.R., Keyhani A., Alimardani R., “Energy use patterns and econometric models of grape production in Hamadan province of Iran”, Energy, 36: 6345–6351, (2011).
  • Khoshnevisan B., Rafiee S., Omid M., Yousefi M., Movahedi M. “Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks”. Energy, 52: 333–338, (2013b.).
  • Taghavifar H., Mardani A., “Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network”, Journal of Cleaner Production, 87: 159–167, (2015).
  • Nabavi-Pelesaraei A., Abdi R., Rafiee S., “Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems”, Journal of the Saudi Society of Agricultural Sciences, 15: 38–47, (2016a.).
  • MGM., “T.C. Tarım Orman Bakanlığı Meteoroloji Genel Müdürlüğü”, https://www.mgm.gov.tr/?il=Mersin,(2017).
  • Anonymous, https://www.turkiye-rehberi.net/mersin-haritasi.asp, (2018).
  • Newbold P., “Statistics for business and economics”, Prentice-Hall, Inc., (1994).
  • TİGEM., “T.C. Tarım İşletmeleri Genel Müdürlüğü”, https://www.tigem.gov.tr/Anasayfa/IndexTR, (2018).
  • Yamane T., “Elementary sampling theory”, Englewood Cliffs, NJ, USA: Prentice Hall;, pp.405, (1967).
  • Omidi-Arjenaki O., Ebrahimi R., Ghanbarian D., “Analysis of energy input and output for honey production in Iran (2012–2013)”, Renewable and Sustainable Energy Reviews, 59: 952–957, (2016).
  • Singh H., Mishra D., Nahar N.M., “Energy use pattern in production agriculture of a typical village in Arid Zone India-Part I.”, Energy Convers Manage, 43(16):2275-2286, (2002).
  • Canakcı M., Akıncı I., “Energy use pattern analyses of greenhouse vegetable production”, Energy; 31:1243-1256, (2006).
  • Nabavi-Pelesaraei A., Rafiee S., Mohtasebi S.S., Hosseinzadeh-Bandbafha H., Chau K., “Energy consumption enhancement and environmental life cycle assessment in paddy production using optimization techniques”, Journal of Cleaner Production, 162: 571–586, (2017).
  • Mani I., Kumar P., Panwar J.S., Kant K., “Variation in energy consumption in production of wheat- maize with varying altitudes in Hilly Regions of Himachal Pradesh, India”, Energy, 32: 2336-2339, (2007)
  • Hosseinzadeh-Bandbafha H, Nabavi-Pelesaraei A, Khanali M, Ghahderijani M, Chau K-W., “Application of data envelopment analysis approach for optimization of energy use and reduction of greenhouse gas emission in peanut production of Iran”, Journal of Cleaner Production, 172: 1327–1335, (2018).
  • Nabavi-Pelesaraei A, Rafiee S, Saeid Mohtasebi S, Hosseinzadeh-Bandbafha H, Chau K-W., “Assessment of optimized pattern in milling factories of rice production based on energy, environmental and economic objectives”, Energy, 169: 1259–1273, (2019).
  • Rafiee S., Mousavi-Avval S.H., Mohammadi A., “Modeling and sensitivity analysis of energy inputs for apple production in Iran”, Energy, 35: 3301-3306, (2010).
  • Taki M., Ajabshirchi Y., Mobtaker H.G., Abdi R., “Energy consumption, input-output relationship and cost analysis for greenhouse productions in Esfahan province of Iran”, American Journal of Experimental Agriculture, 2(3): 485-501,( 2012).
  • Nabavi-Pelesaraei A., Abdi R., Rafiee S., Shamshirband S., Yousefinejad-Ostadkelayeh M., “Resource management in cropping systems using artificial intelligence techniques: a case study of orange orchards in north of Iran”, Stochastic Environmental Research and Risk Assessment, 30: 413–427, (2016b).
  • Anonymous, https://www.ardamavi.com/2017/07/sinir-aglari.html, (2019).
  • Pahlavan R., Omid M., Akram A., “Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production”, Energy, 37: 171–176, (2012).
  • Ranganathan A., “The Levenberg-Marquardt Algorithm”,http://www.ananth.in/Notes_files/lmtut.pdf, (2004).
  • Zhao Z., Chow T.L., Rees H.W., Yang Q., Xing Z., Meng F.R., “Predict soil texture distributions using an artificial neural network model”, Comput. Electron. Agric., 65 (1), 36-48, (2009).
  • Deh Kiani, M.K., Ghobadian B., Tavakoli T., Nikbakht A.M., Najafi G., “Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends”, Energy, 35 (1), 65-69, (2010).
  • Landeras G., Ortiz-Barredo A., Lopez J.J., “Comparison of Artificial Neural Network Models and Empirical and Semi-Empirical Equations For Daily Reference Evapotranspiration Estimation in the Basque Country (Northem Spain)”, Agricultural Water Management, 95:553-565, (2008).
  • Traore S., Wang Y.M., Kerh T., “Artificial Neural Network for Modeling Reference Evapotranspiration Complex Process in Sudano-Sahelian Zone”, Agricultural Water Management, AGWAT-294,; No of Page 8., (2010).
  • Trejo-Perea M., Herrera-Ruiz G., Rıos- Moreno J., Miranda R.C., Rivas-Arazia E., “Greenhouse Energy Consumption Prediction using Neural Networks Models”, Int. J. Agric. Biol., Mexico, Vol. 11, No. 1, (2009).
  • Heidari M.D., Omid M., “Energy use patterns and econometric models of major greenhouse vegetable productions in Iran”, Energy, 36: 220–225, (2011).
  • Mohammadi A., Omid M., “Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran”, Applied Energy, 87: 191–196, (2010).

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

Yıl 2021, Cilt: 24 Sayı: 1, 151 - 160, 01.03.2021
https://doi.org/10.2339/politeknik.680921

Öz

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.

Kaynakça

  • Kurtar E.S., Balkaya A., Göçmen M., Karaağaç O., “Hıyara (Cucumis sativus L.) anaç olabilecek kabak (Cucurbita spp.) genotiplerinde ışınlanmış polen tekniği ile dihaploidizasyon”,Selçuk Tarım ve Gıda Bilimleri Dergisi, 31 (1), 34-41, (2017).
  • Li X.Z., Chen S.X., “Screening and Identification of Cucumber Germplasm and Rootstock Resistance against the Root-Knot Nematode (Meloidogyne incognita)”, Genetics and Molecular Research, 16 (2), gmr16029383, (2017).
  • Anonymous, 2017a., “http://www.fao.org/faostat/en/#data/QC” (Erişim tarihi: 25.04.2017)
  • Pishgar-Komleh S.H., Omid M., Heidari M.D., “On the study of energy use and GHG (greenhouse gas) emissions in greenhouse cucumber production in Yazd province”. Energy, 59: 63–71, (2013).
  • Taki M., Yildizhan H., “ Evaluation the sustainable energy applications for fruit and vegetable productions processes; case study”, Greenhouse cucumber production. Journal of Cleaner Production, 199: 164–172, (2018).
  • Rohani A., Taki M., Abdollahpour M.A.. “Novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)”, Renewable Energy, 115 :411–422, 2018.
  • Anonymous, “Batı Akdeniz Kalkınma Ajansı (Antalya-Isparta-Burdur) Antalya’da Tarım Sektörünün Sorunları Ve Çözüm Önerileri Çalıştayı”, Örtüaltı Sebzecilik Alt Sektörü, Çalışma Grubu Raporu, (2010).
  • Sevgican A.Y., Tüzel, A., Gül, R.Z., “Türkiye'de Örtüaltı Yetistiriciligi Türkiye Ziraat Mühendisligi V. Teknik Kongresi”, Cilt: 2, 679-707, (2000).
  • Anonymous, 2017b., http://bahcebitkileri.cu.edu.tr/upload/nturemis/turkiyeortualti.pdf (Erişim tarihi: 15.09.2017).
  • Öztemel E., “Yapay Sinir ağları”, Papatya Yayınları 3. Baskı, (2012).
  • Safa M., Samarasinghe S., “Determination and modelling of energy consumption in wheat production using neural networks: A case study in canterbury province, Newzealand”, Energy, 36: 5140- 5147,(2011).
  • Haykin S., “Neural Networks, A Comprehensive Foundation”, Macmillan College Publishing Company, Inc., New Jersey. , (1994).
  • Özcalik H.R., Kucuktufekci A., “Dinamik Sistemlerin Yapay Sinir Ağları ile Düz ve Ters Modellenmesi”, KSÜ Fen ve Mühendislik Dergisi,6(1), 26-35, (2003).
  • Yazdani M., Saghafian B., Mahdian M., Soltani S., “Monthly runoff estimation using artificial neural Networks”, Journal of Agricultural Science and Technology, 11: 335–362, (2009).
  • Farjam A., Omid M., Akaram A., Fazel Niari Z., “A neural network based modeling and sensitivity analysis of energy inputs for predicting seed and grain corn yields”, Journal of Agricultural Science and Technology, 16: 767–778, (2014).
  • Gezer I., Acaroglu M., Haciseferogullari H., “Use of energy and labor in apricot agriculture in Turkey”, Bioenergy ,24 (3), 215-219, (2003).
  • Mohammadi A., Rafiee S., Mohtasebi S.S., Mousavi-Avval S.H., Rafiee H., “Energy inputs – yield relationship and cost analysis of kiwifruit production in Iran”, Renew. Energy, 35, 1071¬1075, (2010).
  • Rahman M.M., Bala B.K., “Modelling of jute production using artificial neural networks”. Biosys. Eng., 105 (3), 350-356, (2010).
  • Tabatabaie S.M.H., Rafiee S., Keyhani A., Ebrahimi A., “Energy and economic assessment of prune production in Tehran province of Iran”, J. Clean. Prod., 39, 280-284, (2013).
  • Khoshnevisan B., Rafiee B., Omid M., Mousazadeh H., “Prognostication of environmental indices in potato production using artificial neural networks”, J. Clean. Prod. 52, 402-409, (2013a.).
  • Ozkan B., Fert C., Karadeniz C.F., “Energy and cost analysis for greenhouse and open-field grape Production”, Energy, 32: 1500–1504, (2007).
  • Zangeneh M., Omid M., Akram A., “Assessment of machinery energy ratio in potato production by means of artificial neural network”, African Journal of Agricultural Research, 5: 993–998, (2010).
  • Houshyar E., Sheikh Davoodi M., Bahrami H., Kiani S., Houshyar M., “Energy use forecasting for wheat production utilizing artificial neural network”, Word Applied Science Journal, 10: 958–962, (2010).
  • Zangeneh M., Omid M., Akram A., “A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran”, Spanish Journal of Agricultural Research, 3: 661–671, (2011).
  • Hamedani S.R., Keyhani A., Alimardani R., “Energy use patterns and econometric models of grape production in Hamadan province of Iran”, Energy, 36: 6345–6351, (2011).
  • Khoshnevisan B., Rafiee S., Omid M., Yousefi M., Movahedi M. “Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks”. Energy, 52: 333–338, (2013b.).
  • Taghavifar H., Mardani A., “Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network”, Journal of Cleaner Production, 87: 159–167, (2015).
  • Nabavi-Pelesaraei A., Abdi R., Rafiee S., “Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems”, Journal of the Saudi Society of Agricultural Sciences, 15: 38–47, (2016a.).
  • MGM., “T.C. Tarım Orman Bakanlığı Meteoroloji Genel Müdürlüğü”, https://www.mgm.gov.tr/?il=Mersin,(2017).
  • Anonymous, https://www.turkiye-rehberi.net/mersin-haritasi.asp, (2018).
  • Newbold P., “Statistics for business and economics”, Prentice-Hall, Inc., (1994).
  • TİGEM., “T.C. Tarım İşletmeleri Genel Müdürlüğü”, https://www.tigem.gov.tr/Anasayfa/IndexTR, (2018).
  • Yamane T., “Elementary sampling theory”, Englewood Cliffs, NJ, USA: Prentice Hall;, pp.405, (1967).
  • Omidi-Arjenaki O., Ebrahimi R., Ghanbarian D., “Analysis of energy input and output for honey production in Iran (2012–2013)”, Renewable and Sustainable Energy Reviews, 59: 952–957, (2016).
  • Singh H., Mishra D., Nahar N.M., “Energy use pattern in production agriculture of a typical village in Arid Zone India-Part I.”, Energy Convers Manage, 43(16):2275-2286, (2002).
  • Canakcı M., Akıncı I., “Energy use pattern analyses of greenhouse vegetable production”, Energy; 31:1243-1256, (2006).
  • Nabavi-Pelesaraei A., Rafiee S., Mohtasebi S.S., Hosseinzadeh-Bandbafha H., Chau K., “Energy consumption enhancement and environmental life cycle assessment in paddy production using optimization techniques”, Journal of Cleaner Production, 162: 571–586, (2017).
  • Mani I., Kumar P., Panwar J.S., Kant K., “Variation in energy consumption in production of wheat- maize with varying altitudes in Hilly Regions of Himachal Pradesh, India”, Energy, 32: 2336-2339, (2007)
  • Hosseinzadeh-Bandbafha H, Nabavi-Pelesaraei A, Khanali M, Ghahderijani M, Chau K-W., “Application of data envelopment analysis approach for optimization of energy use and reduction of greenhouse gas emission in peanut production of Iran”, Journal of Cleaner Production, 172: 1327–1335, (2018).
  • Nabavi-Pelesaraei A, Rafiee S, Saeid Mohtasebi S, Hosseinzadeh-Bandbafha H, Chau K-W., “Assessment of optimized pattern in milling factories of rice production based on energy, environmental and economic objectives”, Energy, 169: 1259–1273, (2019).
  • Rafiee S., Mousavi-Avval S.H., Mohammadi A., “Modeling and sensitivity analysis of energy inputs for apple production in Iran”, Energy, 35: 3301-3306, (2010).
  • Taki M., Ajabshirchi Y., Mobtaker H.G., Abdi R., “Energy consumption, input-output relationship and cost analysis for greenhouse productions in Esfahan province of Iran”, American Journal of Experimental Agriculture, 2(3): 485-501,( 2012).
  • Nabavi-Pelesaraei A., Abdi R., Rafiee S., Shamshirband S., Yousefinejad-Ostadkelayeh M., “Resource management in cropping systems using artificial intelligence techniques: a case study of orange orchards in north of Iran”, Stochastic Environmental Research and Risk Assessment, 30: 413–427, (2016b).
  • Anonymous, https://www.ardamavi.com/2017/07/sinir-aglari.html, (2019).
  • Pahlavan R., Omid M., Akram A., “Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production”, Energy, 37: 171–176, (2012).
  • Ranganathan A., “The Levenberg-Marquardt Algorithm”,http://www.ananth.in/Notes_files/lmtut.pdf, (2004).
  • Zhao Z., Chow T.L., Rees H.W., Yang Q., Xing Z., Meng F.R., “Predict soil texture distributions using an artificial neural network model”, Comput. Electron. Agric., 65 (1), 36-48, (2009).
  • Deh Kiani, M.K., Ghobadian B., Tavakoli T., Nikbakht A.M., Najafi G., “Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends”, Energy, 35 (1), 65-69, (2010).
  • Landeras G., Ortiz-Barredo A., Lopez J.J., “Comparison of Artificial Neural Network Models and Empirical and Semi-Empirical Equations For Daily Reference Evapotranspiration Estimation in the Basque Country (Northem Spain)”, Agricultural Water Management, 95:553-565, (2008).
  • Traore S., Wang Y.M., Kerh T., “Artificial Neural Network for Modeling Reference Evapotranspiration Complex Process in Sudano-Sahelian Zone”, Agricultural Water Management, AGWAT-294,; No of Page 8., (2010).
  • Trejo-Perea M., Herrera-Ruiz G., Rıos- Moreno J., Miranda R.C., Rivas-Arazia E., “Greenhouse Energy Consumption Prediction using Neural Networks Models”, Int. J. Agric. Biol., Mexico, Vol. 11, No. 1, (2009).
  • Heidari M.D., Omid M., “Energy use patterns and econometric models of major greenhouse vegetable productions in Iran”, Energy, 36: 220–225, (2011).
  • Mohammadi A., Omid M., “Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran”, Applied Energy, 87: 191–196, (2010).
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Bekir Yelmen 0000-0001-7655-530X

Mutlu Tarık Çakır Bu kişi benim 0000-0002-0107-594X

Hande Havva Şahin Bu kişi benim 0000-0003-2619-4993

Cengiz Kurt Bu kişi benim 0000-0002-1148-9900

Yayımlanma Tarihi 1 Mart 2021
Gönderilme Tarihi 28 Ocak 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 24 Sayı: 1

Kaynak Göster

APA Yelmen, B., Çakır, M. T., Şahin, H. H., Kurt, C. (2021). Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi. Politeknik Dergisi, 24(1), 151-160. https://doi.org/10.2339/politeknik.680921
AMA Yelmen B, Çakır MT, Şahin HH, Kurt C. Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi. Politeknik Dergisi. Mart 2021;24(1):151-160. doi:10.2339/politeknik.680921
Chicago Yelmen, Bekir, Mutlu Tarık Çakır, Hande Havva Şahin, ve Cengiz Kurt. “Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi”. Politeknik Dergisi 24, sy. 1 (Mart 2021): 151-60. https://doi.org/10.2339/politeknik.680921.
EndNote Yelmen B, Çakır MT, Şahin HH, Kurt C (01 Mart 2021) Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi. Politeknik Dergisi 24 1 151–160.
IEEE B. Yelmen, M. T. Çakır, H. H. Şahin, ve C. Kurt, “Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi”, Politeknik Dergisi, c. 24, sy. 1, ss. 151–160, 2021, doi: 10.2339/politeknik.680921.
ISNAD Yelmen, Bekir vd. “Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi”. Politeknik Dergisi 24/1 (Mart 2021), 151-160. https://doi.org/10.2339/politeknik.680921.
JAMA Yelmen B, Çakır MT, Şahin HH, Kurt C. Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi. Politeknik Dergisi. 2021;24:151–160.
MLA Yelmen, Bekir vd. “Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi”. Politeknik Dergisi, c. 24, sy. 1, 2021, ss. 151-60, doi:10.2339/politeknik.680921.
Vancouver Yelmen B, Çakır MT, Şahin HH, Kurt C. Yapay Sinir Ağı (YSA) Kullanarak Sera Sistemlerinde Enerji Verimliliğinin Modellenmesi. Politeknik Dergisi. 2021;24(1):151-60.
 
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