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Performance Analysis of Ground Source Heat Pump With Artificial Neural Networks Approach

Yıl 2024, Cilt: 39 Sayı: 1, 57 - 72, 28.03.2024

Netice Duman Ahmet Gürkan Yüksek Mustafa Caner Ertan Buyruk

https://doi.org/10.21605/cukurovaumfd.1459370

Öz

Heat pumps are efficient and accessible alternatives to conventional systems used for cooling and heating in buildings. Ground Source Heat Pumps (GSHP), using ground heat as the heat source, are promising technologies to meet heating and cooling loads in a clean and sustainable way. GSHP is a system with high installation and operating costs. For this reason, it is very important that performance analyzes can be made without installing the GSHP system, which is suitable for use in different sectors in terms of efficiency. An artificial neural network (ANN) model is proposed to predict the performance of the heat pump and system and the heat removed from the condenser, starting from the approach of evaluating the performance values with models from which the systems can be estimated before they are installed. Regression analysis with artificial neural networks is a machine learning method that has the ability to learn complex relationships between input and output data and can effectively model non-linear relationships in problems. The data measured by the established in Sivas province experimental system are separated as training data and test data to train the ANN and in the first stage of the model, the training data; In the second stage, test data was used. In the presented study, the applicability of artificial neural networks, which have been used in various applications to estimate the coefficient of performance of horizontal GSHP, and which have been shown to be especially useful in system modeling and system description, has been demonstrated. As a result of this study, it was determined that the COP R2 value of the heat pump was 0,9733, the COP R2 value of the TKIP system was 0,9896, and the R2 value of the ANN model of the heat removed from the condenser was 0,9878. Based on the statistical determinants produced, it was concluded that ANNs can be used for COP estimation as an accurate method in the GSHP system.

Anahtar Kelimeler

Ground source heat pump Performance analysis Heating Artificial neural network Regression analysis

Kaynakça

  • 1. Chua, K.J., Chou, S.K., Yang, W.M., 2010. Advances in Heat Pump Systems: A Review, Applied Energy, 87(12), 3611-3624.
  • 2. Xu, X., Liu, J., Wang, Y., Xu, J., Bao, J., 2020. Performance Evaluation of Ground Source Heat Pump Using Linear and Nonlinear Regressions and Artificial Neural Networks. Applied Thermal Engineering, 180, 115914.
  • 3. Yeşilbaş, D., Güven, A., 2021. Kütle Spektrometresi Verileri Kullanılarak Yumurtalık Kanserinin Yapay Sinir Ağlarıyla Sınıflandırılması. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(3), 781-790.
  • 4. Ozbek, A., 2016. Yapay Sinir Ağları Kullanarak Nemli Havanın Termodinamik Özelliklerinin Tahmini. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 31(1), 51-58.
  • 5. Şevik, S., Aktaş, M., Özdemir, M.B., Doğan, H., 2014. Bir Güneş Destekli Isı Pompalı Kurutucuda Mantarın Kurutma Davranışlarının Yapay Sinir Ağı Kullanılarak Modellenmesi. Journal of Agricultural Sciences, 20(2), 187-202.
  • 6. Toktaş, İ., Aktürk, N., 2011. Yapay Sinir Ağları Tabanlı Silindirik Düz Dişli Çark Tasarımı. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi,13(3), 387-395.
  • 7. Mellit, A, Kalogirou, S.A., Hontoria, L., Shaari, S., 2009. Artificial Intelligence Techniques for Sizing Photovoltaic Systems: A Review. Renewable and Sustainable Energy Reviews, 13, 406-419.
  • 8. Kalogirou, S.A., 2001. Artificial Neural Networks in Renewable Energy Systems Applications: A Review. Renewable and Sustainable Energy Reviews, 5, 373-401.
  • 9. Mohanraj, M., Jayaraj, S., Muraleedharan, C., 2012. Applications of Artificial Neural Networks for Refrigeration Air-conditioning and Heat Pump Systems: A Review. Renewable and Sustainable Energy Reviews, 16(2), 1340-1358.
  • 10. Esen, H., Inalli, M., Esen, M., Pihtili, K., 2007. Energy and Exergy Analysis of a Ground-Coupled Heat Pump System with Two Horizontal Ground Heat Exchangers, Building and Environment, 42(10), 3606-3615.
  • 11. Balbay, A., Esen, M., 2013. Temperature Distributions in Pavement and Bridge Slabs Heated by Using Vertical Ground-Source Heat Pump Systems. Acta Scientiarum. Technology, 35(4), 677-685.
  • 12. Marmaras, J., Burbank, J., Kosanovic, D.B., 2016. Primary-Secondary De-Coupled Ground Source Heat Pump Systems Coefficient of Performance Optimization Through Entering Water Temperature Control. Applied Thermal Engineering, 96, 107-116.
  • 13. Zheng, T., Shao, H., Schelenz, S., Hein, P., Vienken, T., Pang, Z., Nagel, T., 2016. Efficiency and Economic Analysis of Utilizing Latent Heat from Groundwater Freezing in the Context of Borehole Heat Exchanger Coupled Ground Source Heat Pump Systems. Applied Thermal Engineering, 105, 314-326.
  • 14. Arcaklıoğlu, E., Erisen, A., Yilmaz, R., 2004. Artificial Neural Network Analysis of Heat Pumps Using Refrigerant Mixtures. Energy Conversion Management, 45(11-12), 1917-1929.
  • 15. Esen, H., Inalli, M., Sengur, A., Esen, M., 2008. Performance Prediction of a Ground-Coupled Heat Pump System Using Artificial Neural Networks. Expert Systems with Applications, 35(4), 1940-1948.
  • 16. Esen, H., Inalli, M., 2009. Modelling of a Vertical Ground Coupled Heat Pump System by Using Artificial Neural Networks. Expert Systems with Applications, 36(7), 10229-10238.
  • 17. Esen, H., Inalli, M., Sengur, A., Esen, M., 2008. Forecasting of a Ground-Coupled Heat Pump Performance Using Neural Networks with Statistical Data Weighting Pre-Processing. International Journal of Thermal Sciences, 47(4), 431-441.
  • 18. Mohanraj, M., Jayaraj, S., Muraleedharan, C., 2009. Performance Prediction of a Direct Expansion Solar Assisted Heat Pump Using Artificial Neural Networks. Applied Energy, 86(9), 1442-1449.
  • 19. Wang, G., Zhang, Y., Wang, R., Han, G., 2013. Performance Prediction of Ground-Coupled Heat Pump System Using NNCA-RBF Neural Networks. In 2013 25th Control and Decision Conference (CCDC), Chinese, 2164-2169.
  • 20. Benli, H., 2016. Performance Prediction Between Horizontal and Vertical Source Heat Pump Systems for Greenhouse Heating with the Use of Artificial Neural Networks. Heat and Mass Transfer, 52(8), 1707-1724.
  • 21. Sun, W., Hu, P., Lei, F., Zhu, N., Jiang, Z., 2015. Case Study of Performance Evaluation of Ground Source Heat Pump System Based on ANN and ANFIS Models. Applied Thermal Engineering, 87, 586-594.
  • 22. Park, S.K., Moon, H.J., Min, K.C., Hwang, C., Kim, S., 2018. Application of a Multiple Linear Regression and an Artificial Neural Network Model for the Heating Performance Analysis and Hourly Prediction of a Large-Scale Ground Source Heat Pump System. Energy and Buildings, 165, 206-215.
  • 23. Puttige, A.R., Andersson, S., Östin, R., Olofsson, T., 2021. Application of Regression and ANN Models for Heat Pumps with Field Measurements. Energies, 14(6), 1750.
  • 24. Shin, J., Cho, Y., 2021. Machine-Learning Based Coefficient of Performance Prediction Model for Heat Pump Systems. Applied Sciences, 12(1), 362.
  • 25. Liu, Y., Mei, X., Zhang, G., Cao, Z., 2023. Long-term Performance Prediction of Ground Source Heat Pump System Based on Co-simulation and Artificial Neural Network. Journal of Building Engineering, 79, 107949.
  • 26. Caner, M., 2018. Yatay Tip Toprak Kaynaklı Isı Pompası Sisteminin Sivas Şartlarında Değerlendirilmesi. Yüksek Lisans Tezi, Sivas Cumhuriyet Üniversitesi, Fen Bilimleri Enstitüsü, Makine Mühendisliği Anabilim Dalı, Sivas, Türkiye, 111.
  • 27. Kubat, M., 1999. Neural Networks: A Comprehensive Foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4), 409-412, 823.
  • 28. Fitch, F.B., 1944. McCulloch Warren S. and Pitts Walter, A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 115-133.
  • 29. Fausett, L.V., 1994. Fundamentals of Neural Networks Architectures Algorithms and Applications, Englewood Cliffs. NJ: Prentice-Hall, 476.
  • 30. Gurney, K., 1997. An Introduction to Neural Networks. UCL Press SBN 0-203-45151-1, 317.
  • 31. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning Representations by Back-propagating Errors. Nature, 323(6088), 533-536.
  • 32. Graupe, D., 2007, Principles of Artificial Neural Networks. World Scientific, 6, 303.
  • 33. Ruder, S., 2016. An Overview of Gradient Descent Optimization Algorithms. arXiv preprint arXiv:1609.04747.
  • 34. Hu. X., 2003. DB-HReduction: A Data Preprocessing Algorithm for Data Mining Applications. Applied Mathematics Letters, 16(6), 889-895.
  • 35. González-Sopeña, J.M., Pakrashi, V., Ghosh, B., 2021. An Overview of Performance Evaluation Metrics for Short-term Statistical Wind Power Forecasting. Renewable and Sustainable Energy Reviews, 138, 110515.

Yapay Sinir Ağları Yaklaşımı ile Toprak Kaynaklı Isı Pompasının Performans Analizi

Yıl 2024, Cilt: 39 Sayı: 1, 57 - 72, 28.03.2024

Netice Duman Ahmet Gürkan Yüksek Mustafa Caner Ertan Buyruk

https://doi.org/10.21605/cukurovaumfd.1459370

Öz

Isı pompaları, binalarda soğutma ve ısıtma için kullanılan konvansiyonel sistemlere verimli ve ulaşılabilir alternatiflerdir. Isı kaynağı olarak toprak ısısını kullanan toprak kaynaklı ısı pompaları (TKIP), ısıtma ve soğutma yüklerini temiz ve sürdürülebilir bir şekilde karşılamak için umut verici teknolojilerdir. TKIP, kurulum ve işletme maliyetleri yüksek olan bir sistemdir. Bu nedenle verimlilik açısından farklı sektörlerde kullanımı uygun olan TKIP sistemini kurmadan performans analizleri yapılabilir olması çok önemlidir. Sistemler kurulmadan önce performans değerlerinin tahmin edilebilecek olduğu modeller ile değerlendirilmesi yaklaşımdan yola çıkılarak, ısı pompası ve sistemin performansı ve yoğuşturucudan atılan ısıyı tahmin etmek için bir yapay sinir ağı (YSA) modeli önerilmektedir. Yapay sinir ağları ile regresyon analizi, girdi ve çıkış verileri arasındaki karmaşık ilişkileri öğrenme yeteneğine sahip bir makine öğrenimi yöntemidir ve problemlerindeki non-lineer ilişkileri etkili bir şekilde modelleyebilir. Sivas ilinde Kurulan deneysel sistem ile ölçülen veriler, YSA'yı eğitmek için eğitim verisi ve test verisi olarak ayrılmıştır ve modelin ilk aşamasında eğitim verisi; ikinci aşamasında ise test verisi kullanılmıştır. Sunulan çalışmada, yatay TKIP’ın performans katsayısını tahmin etmek için çeşitli uygulamalarda kullanılmış ve özellikle sistem modelleme ve sistem tanımlamada yararlı oldukları gösterilmiş yapay sinir ağlarının uygulanabilirliği ortaya konulmuştur. Bu çalışmanın sonucunda, ısı pompası COP R2 değeri 0,9733, TKIP sistemi COP R2 değeri 0,9896 ve yoğuşturucudan atılan ısının YSA modelinin R2 değeri 0,9878 olduğu tespit edilmiştir. Üretilen istatistiksel belirleyiciler üzerinden yola çıkılarak YSA'ların TKIP sisteminde doğru bir yöntem olarak COP tahmini için kullanılabileceği sonucuna varılmıştır.

Anahtar Kelimeler

Toprak kaynaklı ısı pompası Performans analizi Isıtma Yapay sinir ağları Regresyon analizi

Kaynakça

  • 1. Chua, K.J., Chou, S.K., Yang, W.M., 2010. Advances in Heat Pump Systems: A Review, Applied Energy, 87(12), 3611-3624.
  • 2. Xu, X., Liu, J., Wang, Y., Xu, J., Bao, J., 2020. Performance Evaluation of Ground Source Heat Pump Using Linear and Nonlinear Regressions and Artificial Neural Networks. Applied Thermal Engineering, 180, 115914.
  • 3. Yeşilbaş, D., Güven, A., 2021. Kütle Spektrometresi Verileri Kullanılarak Yumurtalık Kanserinin Yapay Sinir Ağlarıyla Sınıflandırılması. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(3), 781-790.
  • 4. Ozbek, A., 2016. Yapay Sinir Ağları Kullanarak Nemli Havanın Termodinamik Özelliklerinin Tahmini. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 31(1), 51-58.
  • 5. Şevik, S., Aktaş, M., Özdemir, M.B., Doğan, H., 2014. Bir Güneş Destekli Isı Pompalı Kurutucuda Mantarın Kurutma Davranışlarının Yapay Sinir Ağı Kullanılarak Modellenmesi. Journal of Agricultural Sciences, 20(2), 187-202.
  • 6. Toktaş, İ., Aktürk, N., 2011. Yapay Sinir Ağları Tabanlı Silindirik Düz Dişli Çark Tasarımı. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi,13(3), 387-395.
  • 7. Mellit, A, Kalogirou, S.A., Hontoria, L., Shaari, S., 2009. Artificial Intelligence Techniques for Sizing Photovoltaic Systems: A Review. Renewable and Sustainable Energy Reviews, 13, 406-419.
  • 8. Kalogirou, S.A., 2001. Artificial Neural Networks in Renewable Energy Systems Applications: A Review. Renewable and Sustainable Energy Reviews, 5, 373-401.
  • 9. Mohanraj, M., Jayaraj, S., Muraleedharan, C., 2012. Applications of Artificial Neural Networks for Refrigeration Air-conditioning and Heat Pump Systems: A Review. Renewable and Sustainable Energy Reviews, 16(2), 1340-1358.
  • 10. Esen, H., Inalli, M., Esen, M., Pihtili, K., 2007. Energy and Exergy Analysis of a Ground-Coupled Heat Pump System with Two Horizontal Ground Heat Exchangers, Building and Environment, 42(10), 3606-3615.
  • 11. Balbay, A., Esen, M., 2013. Temperature Distributions in Pavement and Bridge Slabs Heated by Using Vertical Ground-Source Heat Pump Systems. Acta Scientiarum. Technology, 35(4), 677-685.
  • 12. Marmaras, J., Burbank, J., Kosanovic, D.B., 2016. Primary-Secondary De-Coupled Ground Source Heat Pump Systems Coefficient of Performance Optimization Through Entering Water Temperature Control. Applied Thermal Engineering, 96, 107-116.
  • 13. Zheng, T., Shao, H., Schelenz, S., Hein, P., Vienken, T., Pang, Z., Nagel, T., 2016. Efficiency and Economic Analysis of Utilizing Latent Heat from Groundwater Freezing in the Context of Borehole Heat Exchanger Coupled Ground Source Heat Pump Systems. Applied Thermal Engineering, 105, 314-326.
  • 14. Arcaklıoğlu, E., Erisen, A., Yilmaz, R., 2004. Artificial Neural Network Analysis of Heat Pumps Using Refrigerant Mixtures. Energy Conversion Management, 45(11-12), 1917-1929.
  • 15. Esen, H., Inalli, M., Sengur, A., Esen, M., 2008. Performance Prediction of a Ground-Coupled Heat Pump System Using Artificial Neural Networks. Expert Systems with Applications, 35(4), 1940-1948.
  • 16. Esen, H., Inalli, M., 2009. Modelling of a Vertical Ground Coupled Heat Pump System by Using Artificial Neural Networks. Expert Systems with Applications, 36(7), 10229-10238.
  • 17. Esen, H., Inalli, M., Sengur, A., Esen, M., 2008. Forecasting of a Ground-Coupled Heat Pump Performance Using Neural Networks with Statistical Data Weighting Pre-Processing. International Journal of Thermal Sciences, 47(4), 431-441.
  • 18. Mohanraj, M., Jayaraj, S., Muraleedharan, C., 2009. Performance Prediction of a Direct Expansion Solar Assisted Heat Pump Using Artificial Neural Networks. Applied Energy, 86(9), 1442-1449.
  • 19. Wang, G., Zhang, Y., Wang, R., Han, G., 2013. Performance Prediction of Ground-Coupled Heat Pump System Using NNCA-RBF Neural Networks. In 2013 25th Control and Decision Conference (CCDC), Chinese, 2164-2169.
  • 20. Benli, H., 2016. Performance Prediction Between Horizontal and Vertical Source Heat Pump Systems for Greenhouse Heating with the Use of Artificial Neural Networks. Heat and Mass Transfer, 52(8), 1707-1724.
  • 21. Sun, W., Hu, P., Lei, F., Zhu, N., Jiang, Z., 2015. Case Study of Performance Evaluation of Ground Source Heat Pump System Based on ANN and ANFIS Models. Applied Thermal Engineering, 87, 586-594.
  • 22. Park, S.K., Moon, H.J., Min, K.C., Hwang, C., Kim, S., 2018. Application of a Multiple Linear Regression and an Artificial Neural Network Model for the Heating Performance Analysis and Hourly Prediction of a Large-Scale Ground Source Heat Pump System. Energy and Buildings, 165, 206-215.
  • 23. Puttige, A.R., Andersson, S., Östin, R., Olofsson, T., 2021. Application of Regression and ANN Models for Heat Pumps with Field Measurements. Energies, 14(6), 1750.
  • 24. Shin, J., Cho, Y., 2021. Machine-Learning Based Coefficient of Performance Prediction Model for Heat Pump Systems. Applied Sciences, 12(1), 362.
  • 25. Liu, Y., Mei, X., Zhang, G., Cao, Z., 2023. Long-term Performance Prediction of Ground Source Heat Pump System Based on Co-simulation and Artificial Neural Network. Journal of Building Engineering, 79, 107949.
  • 26. Caner, M., 2018. Yatay Tip Toprak Kaynaklı Isı Pompası Sisteminin Sivas Şartlarında Değerlendirilmesi. Yüksek Lisans Tezi, Sivas Cumhuriyet Üniversitesi, Fen Bilimleri Enstitüsü, Makine Mühendisliği Anabilim Dalı, Sivas, Türkiye, 111.
  • 27. Kubat, M., 1999. Neural Networks: A Comprehensive Foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4), 409-412, 823.
  • 28. Fitch, F.B., 1944. McCulloch Warren S. and Pitts Walter, A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 115-133.
  • 29. Fausett, L.V., 1994. Fundamentals of Neural Networks Architectures Algorithms and Applications, Englewood Cliffs. NJ: Prentice-Hall, 476.
  • 30. Gurney, K., 1997. An Introduction to Neural Networks. UCL Press SBN 0-203-45151-1, 317.
  • 31. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning Representations by Back-propagating Errors. Nature, 323(6088), 533-536.
  • 32. Graupe, D., 2007, Principles of Artificial Neural Networks. World Scientific, 6, 303.
  • 33. Ruder, S., 2016. An Overview of Gradient Descent Optimization Algorithms. arXiv preprint arXiv:1609.04747.
  • 34. Hu. X., 2003. DB-HReduction: A Data Preprocessing Algorithm for Data Mining Applications. Applied Mathematics Letters, 16(6), 889-895.
  • 35. González-Sopeña, J.M., Pakrashi, V., Ghosh, B., 2021. An Overview of Performance Evaluation Metrics for Short-term Statistical Wind Power Forecasting. Renewable and Sustainable Energy Reviews, 138, 110515.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka (Diğer), Makine Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Netice Duman 0000-0002-9926-8511

Ahmet Gürkan Yüksek 0000-0001-7709-6360

Mustafa Caner 0000-0002-3674-7881

Ertan Buyruk 0000-0002-6539-7614

Yayımlanma Tarihi 28 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 39 Sayı: 1

Kaynak Göster

APA Duman, N., Yüksek, A. G., Caner, M., Buyruk, E. (2024). Yapay Sinir Ağları Yaklaşımı ile Toprak Kaynaklı Isı Pompasının Performans Analizi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 39(1), 57-72. https://doi.org/10.21605/cukurovaumfd.1459370
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