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Solar Radiation Modeling for Turkey Using Atmospheric Parameters with Artificial Neural Networks

Yıl 2016, Cilt: 31 Sayı: 2, 179 - 186, 15.12.2016
https://doi.org/10.21605/cukurovaummfd.310145

Öz

Artificial neural network (ANN) method was applied for modeling and prediction of mean solar radiation in given atmospheric parameters (temperature, pressure, humidity, precipitable water and month) in Turkey (26–45ºE and 36–42ºN) during the period of 2004–2006. Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the network, meteorological measurements taken by the Turkish State Meteorological Service (TSMS) and Wyoming University for the period from 2004 to 2006 from five stations (Adana, Ankara, İstanbul, İzmir, Samsun) distributed in Turkey were used as training and testing data. Data from years 2004 and 2005 were used for training, while the year 2006 was used for testing and validating the model. Solar radiation is the output.

Kaynakça

  • 1. Cuomo, V., Tramutoli, V., Pergola, N., Pietrapertosa, C., Romano, F., 1997. In Place Merging of Satellite Based Atmospheric Water Vapour Measurements. International Journal of Remote Sensing, 18(17), 3649-3668.
  • 2. Lam, J,C., Yang, L., Liu, J., 2006. Development of Passive Design Zones in China Using Bioclimatic Approach. Energy Conversion and Management, 47(6), 746–762.
  • 3. Kaygusuz, K., Sarı, A., 2003. Renewable Energy Potential and Utilization in Turkey. Energy Conversion and Management, 44(3), 459-478.
  • 4. Kaygusuz, K., Ayhan, T., 1999. Analysis of Solar Radiation Data for Trabzon, Turkey. Energy Conversion and Management, 40(5), 545-556.
  • 5. Tymvios, F.S., Jacovides, C.P., Michaelieds, S.C., Scouteli, C., 2005. Comparative study of Angström’s and Artificial Neural Networks Methodologies in Estimating Global Solar Radiation. Solar Energy, 78, 752-762.
  • 6. Mohandes, M., Rehman, S., Halawani, T.O., 1998. Estimation of Global Solar Radiation Using Artificial Neural Networks. Renewable Energy, 14(1-4), 179-184.
  • 7. Mohandes, M., Balghonaim, A., Kassas, M., Rehman, S., Halawani, T.O., 2000. Use of Radial Basis Functions for Estimating Monthly Mean Daily Solar Radiation. Solar Energy, 68(2), 161-168.
  • 8. Lopez, G., Rubio, M.A., Martinez, M., Batlles, F.J., 2001. Estimation of Hourly Global Photosynthetically Active Radiation Using Artificial Neural Network Models. Agricultural and Forest Meteorology, 107(4), 279-291.
  • 9. Hontoria, L., Aguilera, J., Zuria, P., 2005. An Application of the Multilayer Perception: Solar Radiation Maps in Spain. Solar Energy, 79(5), 523–530.
  • 10. Bulut, H., 2004. Typical Solar Radiation Year for Southeastern Anatolia. Renewable Energy, 29, 1477-1488.
  • 11. Şaylan, L., Şen, O., Toros, H., Arısoy, A., 2003. Solar Energy Potential for Heating Cooling Systems in Big Cities of Turkey. Energy Conversion and Management, 43, 1829-1837.
  • 12. Dinçer, I., Dilmaç, S., Ture, I.E., Edin, M., 1996. A Simple Technique for Estimating Solar Radiation Parameters and its Application for Gebze. Energy Conversion and Management, 37(2), 183-198.
  • 13. Tymvios, F.S., Jacovides, C.P., Michaelieds, S.C., Scouteli, C., 2005. Comparative Study of Angström’s and Artificial Neural Networks Methodologies in Estimating Global Solar Radiation. Solar Energy, 78, 752-762.
  • 14. Joseph, C.L., Kevin, K.W.W., Liu, Y., 2008. Solar Radiation Modelling Using ANNs for Different Climates in China. Energy Conversion and Management, 49(5): 1080-1090.
  • 15. Lopez, G., Rubio, M.A., Martinez, M., Batlles, F.J., 2001. Estimation of Hourly Global Photosynthetically Active Radiation Using Artificial Neural Network Models. Agricultural and Forest Meteorology, 07: 279-291.
  • 16. Thornton, P.E., Steven, W.R., 1999. An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity, and Precipitation. Agricultural and Forest Meteorology, 93: 211-228.
  • 17. Ehnberg, J.S.G., Bollen M.H.J., 2005. Simulation of Global Solar Radiation Based on Cloud Observations. Solar Energy, 78:157-162.
  • 18. Dinçer, I., Dilmaç Ş., Ture, I.E., Edin, M., 1996. A Simple Technique for Estimating Solar Radiation Parameters and its Application for Gebze. Energy Conversion and Management, 37(2):183–98.
  • 19. Mohandes, M.A., Halawani, T.O., 2004. Rehman S, Hussain AA. Support Vector Machines for Wind Speed Prediction. Renewable Energy, 29:939–947.
  • 20. Çam, E., Arcaklıoğlu, E., Çavusoğlu, A., Akbıyık, B., 2005. A Classification Mechanism for Determining Average Wind Speed and Power in Several Regions of Turkey Using Artificial Neural Networks. Renewable Energy, 30:227–239.
  • 21. Kalogirou, S.A., 2000. Applications of Artificial Neural-Networks for Energy Systems. Applied Energy, 67:17–35.
  • 22. Haykin, S., 1994. Neural Networks, a Comprehensive Foundation. New Jersey: Prentice-Hall.
  • 23. Melesse, A.M., Hanley, R.S., 2005. Artificial Neural Network Application for Multi-Ecosystem Carbon Flux Simulation. Ecological Model, 189:305–14.
  • 24. Mehmet, B., Besir, Ş., Abdulkadir, Y., 2007. Application of Artificial Neural Networks for the Wind Speed Prediction of Target Station Using Reference Stations Data. Renewable Energy, 32:2350-2360.
  • 25. Şenkal, O., 2015. Solar Radiation and Precipitable Water Modeling for Turkey Using Artificial Neural Networks. Meteorology and Atmospheric Physics, vol. 127(4) 481-488.

Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi

Yıl 2016, Cilt: 31 Sayı: 2, 179 - 186, 15.12.2016
https://doi.org/10.21605/cukurovaummfd.310145

Öz

Yapay sinir ağları (YSA) yöntemi, Türkiye’de (26-45ºE ve 36-42ºN) 2004-2006 dönemlerinde atmosferik parametreler olarak verilen (sıcaklık, basınç, nem, yoğuşmaya geçebilecek su miktarı ve ay) verileri kullanarak ortalama güneş radyasyonun tahmini ve modellenmesi için uygulanmıştır. Levenberg-Marquardt (LM) öğrenme algoritmaları ve logistic sigmoid transfer fonksiyonu ağ içinde kullanılmıştır. Ağı eğitmek amacıyla, Türk Devlet Meteoroloji İşleri Genel Müdürlüğü (DMİ) ve Wyoming Üniversitesi tarafından 2004'den 2006’ye kadar Türkiye'deki beş istasyon (Adana, Ankara, İstanbul, İzmir, Samsun) için alınan meteorolojik ölçümler değerleri eğitim ve test verileri olarak kullanılmıştır. 2004- 2005 yılı verileri eğitim için, 2006 yılı verileri ise test verilerini doğrulamak için kullanılmıştır. Güneş radyasyonu elde edilmiştir.

Kaynakça

  • 1. Cuomo, V., Tramutoli, V., Pergola, N., Pietrapertosa, C., Romano, F., 1997. In Place Merging of Satellite Based Atmospheric Water Vapour Measurements. International Journal of Remote Sensing, 18(17), 3649-3668.
  • 2. Lam, J,C., Yang, L., Liu, J., 2006. Development of Passive Design Zones in China Using Bioclimatic Approach. Energy Conversion and Management, 47(6), 746–762.
  • 3. Kaygusuz, K., Sarı, A., 2003. Renewable Energy Potential and Utilization in Turkey. Energy Conversion and Management, 44(3), 459-478.
  • 4. Kaygusuz, K., Ayhan, T., 1999. Analysis of Solar Radiation Data for Trabzon, Turkey. Energy Conversion and Management, 40(5), 545-556.
  • 5. Tymvios, F.S., Jacovides, C.P., Michaelieds, S.C., Scouteli, C., 2005. Comparative study of Angström’s and Artificial Neural Networks Methodologies in Estimating Global Solar Radiation. Solar Energy, 78, 752-762.
  • 6. Mohandes, M., Rehman, S., Halawani, T.O., 1998. Estimation of Global Solar Radiation Using Artificial Neural Networks. Renewable Energy, 14(1-4), 179-184.
  • 7. Mohandes, M., Balghonaim, A., Kassas, M., Rehman, S., Halawani, T.O., 2000. Use of Radial Basis Functions for Estimating Monthly Mean Daily Solar Radiation. Solar Energy, 68(2), 161-168.
  • 8. Lopez, G., Rubio, M.A., Martinez, M., Batlles, F.J., 2001. Estimation of Hourly Global Photosynthetically Active Radiation Using Artificial Neural Network Models. Agricultural and Forest Meteorology, 107(4), 279-291.
  • 9. Hontoria, L., Aguilera, J., Zuria, P., 2005. An Application of the Multilayer Perception: Solar Radiation Maps in Spain. Solar Energy, 79(5), 523–530.
  • 10. Bulut, H., 2004. Typical Solar Radiation Year for Southeastern Anatolia. Renewable Energy, 29, 1477-1488.
  • 11. Şaylan, L., Şen, O., Toros, H., Arısoy, A., 2003. Solar Energy Potential for Heating Cooling Systems in Big Cities of Turkey. Energy Conversion and Management, 43, 1829-1837.
  • 12. Dinçer, I., Dilmaç, S., Ture, I.E., Edin, M., 1996. A Simple Technique for Estimating Solar Radiation Parameters and its Application for Gebze. Energy Conversion and Management, 37(2), 183-198.
  • 13. Tymvios, F.S., Jacovides, C.P., Michaelieds, S.C., Scouteli, C., 2005. Comparative Study of Angström’s and Artificial Neural Networks Methodologies in Estimating Global Solar Radiation. Solar Energy, 78, 752-762.
  • 14. Joseph, C.L., Kevin, K.W.W., Liu, Y., 2008. Solar Radiation Modelling Using ANNs for Different Climates in China. Energy Conversion and Management, 49(5): 1080-1090.
  • 15. Lopez, G., Rubio, M.A., Martinez, M., Batlles, F.J., 2001. Estimation of Hourly Global Photosynthetically Active Radiation Using Artificial Neural Network Models. Agricultural and Forest Meteorology, 07: 279-291.
  • 16. Thornton, P.E., Steven, W.R., 1999. An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity, and Precipitation. Agricultural and Forest Meteorology, 93: 211-228.
  • 17. Ehnberg, J.S.G., Bollen M.H.J., 2005. Simulation of Global Solar Radiation Based on Cloud Observations. Solar Energy, 78:157-162.
  • 18. Dinçer, I., Dilmaç Ş., Ture, I.E., Edin, M., 1996. A Simple Technique for Estimating Solar Radiation Parameters and its Application for Gebze. Energy Conversion and Management, 37(2):183–98.
  • 19. Mohandes, M.A., Halawani, T.O., 2004. Rehman S, Hussain AA. Support Vector Machines for Wind Speed Prediction. Renewable Energy, 29:939–947.
  • 20. Çam, E., Arcaklıoğlu, E., Çavusoğlu, A., Akbıyık, B., 2005. A Classification Mechanism for Determining Average Wind Speed and Power in Several Regions of Turkey Using Artificial Neural Networks. Renewable Energy, 30:227–239.
  • 21. Kalogirou, S.A., 2000. Applications of Artificial Neural-Networks for Energy Systems. Applied Energy, 67:17–35.
  • 22. Haykin, S., 1994. Neural Networks, a Comprehensive Foundation. New Jersey: Prentice-Hall.
  • 23. Melesse, A.M., Hanley, R.S., 2005. Artificial Neural Network Application for Multi-Ecosystem Carbon Flux Simulation. Ecological Model, 189:305–14.
  • 24. Mehmet, B., Besir, Ş., Abdulkadir, Y., 2007. Application of Artificial Neural Networks for the Wind Speed Prediction of Target Station Using Reference Stations Data. Renewable Energy, 32:2350-2360.
  • 25. Şenkal, O., 2015. Solar Radiation and Precipitable Water Modeling for Turkey Using Artificial Neural Networks. Meteorology and Atmospheric Physics, vol. 127(4) 481-488.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Ozan Şenkal

Yayımlanma Tarihi 15 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 31 Sayı: 2

Kaynak Göster

APA Şenkal, O. (2016). Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 31(2), 179-186. https://doi.org/10.21605/cukurovaummfd.310145
AMA Şenkal O. Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi. cukurovaummfd. Aralık 2016;31(2):179-186. doi:10.21605/cukurovaummfd.310145
Chicago Şenkal, Ozan. “Yapay Sinir Ağları Ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 31, sy. 2 (Aralık 2016): 179-86. https://doi.org/10.21605/cukurovaummfd.310145.
EndNote Şenkal O (01 Aralık 2016) Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 31 2 179–186.
IEEE O. Şenkal, “Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi”, cukurovaummfd, c. 31, sy. 2, ss. 179–186, 2016, doi: 10.21605/cukurovaummfd.310145.
ISNAD Şenkal, Ozan. “Yapay Sinir Ağları Ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 31/2 (Aralık 2016), 179-186. https://doi.org/10.21605/cukurovaummfd.310145.
JAMA Şenkal O. Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi. cukurovaummfd. 2016;31:179–186.
MLA Şenkal, Ozan. “Yapay Sinir Ağları Ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 31, sy. 2, 2016, ss. 179-86, doi:10.21605/cukurovaummfd.310145.
Vancouver Şenkal O. Yapay Sinir Ağları ile Atmosferik Parametreler Kullanılarak Türkiye için Güneş Radyasyonu Modellemesi. cukurovaummfd. 2016;31(2):179-86.