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ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI

Year 2019, , 75 - 88, 01.03.2019
https://doi.org/10.15317/Scitech.2019.183

Abstract

Dünyanın
en önemli enerji kaynağı güneş enerjisi, çeşitli alanlarda farklı konular
altında araştırılmaktadır. Özellikle, fosil yakıt kaynaklarının azalmasından
beri güneş enerjisinin değeri ve önemi daha da artmıştır. Güneş enerjisi ile
ilgili ilk araştırmalar 20. yüzyılın ilk çeyreğinde başlamış ve bu tür
çalışmaların ilki güneş ışınımının güneşlenme süresine karşı tahmin edildiği
çalışmadır. Bu makalenin hedefi, güneş ışınımının tahmini konusunda yeni bir
yöntem niteliğindeki polinom çözümleme (analiz) yolunu sunmak ve uygulamaktır.
Bununla birlikte, polinom çözümlemesi güneş ışınımını tahmin etmekte yetersiz
kalacağından, Polinom ile doğrusal (lineer) bir model özelliğine sahip
Angström-Prescott yaklaşımı önerilmiştir. PoLin (POlinom-LINeer) modelinin
temel ilkesi, salınımı (periyodiklik) veriden ayırmak ve daha sonra Angström-Prescott
modelini arınmış veriye uygulamaktır. Türkiye'nin Güneydoğu Anadolu bölgesi
şehirlerinden Diyarbakır kapsamında sunulan yaklaşımın sonuçları ANFIS, HarLin
ve Angström-Prescott modelleri ile karşılaştırılarak gerekli tavsiyeler
sunulmuştur. PoLin modelinin çıktıları meşhur (klasik) Angström Prescott,
HarLin ve ANFIS modellerinden daha başarılı bulunmuştur.

References

  • Akınoğlu B.G., and Ecevit, A. (1990) Construction of a quadratic model using modified Angström coefficients to estimate global solar radiation, Solar Energy, 45:85–92.
  • Almorox, J., Bocco, M., and Willington, E. (2013) Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina, Renewable Energy, 60:382-387.
  • Amato U., Andretta A., Banoli B., Coluzzi B., Cuomo V., Fontana F., Serio C., (1986) Markov processes and Fourier analysis as a tool describe and simulate Daily solar irradiance. Solar Energy 37(3):179-94.
  • Angström, A. (1924) Solar Terrestrial Radiation, Quarterly Journal of the Royal Meteorological Society, 50:121–126.
  • Baldasano J.M., Clar J, Berna A., (1988) Fourier analaysis of Daily solar radiation data in Spain. Solar Energy 41(4):327-34.
  • Balling R., Cerveny R.S., (1983) Spatial and temporal variations in long-term normal percent possible solar radiation levels in the United States. J Climate Appl Met 22:1726-1732.
  • Benghanem M., Mellit A., and Alamri S.N., (2009) ANN-based modelling and estimation of daily global solar radiation data: A case study, Energy Conversion and Management, 50:1644–1655.
  • Chen, S.X., Gooi, H.B., and Wang M.Q. (2013), Solar radiation forecast based on fuzzy logic and neural networks, Renewable Energy, 60, 195–201.
  • Dogniaux, R., and Lemoine, M. (1983) Classification of radiation sites in terms of different indices of atmospheric transparency. In Palz W. (éditeur), Solar Energy R&D in the European Community, Series F, Vol. 2, Solar Energy Data. D. Reidel Publ. Co., Dordecht, 94-107.
  • Gopinathan, K. K., (1988) A general formula for computing the coefficients of the correlation connecting global solar radiation to sunshine duration, Solar Energy, 41:499-502.
  • Güçlü, Y. S., Dabanlı, İ., and Şişman, E., (2014a) Short- and Long-Term Solar Radiation Estimation Method, Progress in Exergy, Energy, and the Environment, DOI 10.1007/978-3-319-04681-5_48, Springer, Cham.
  • Güçlü, Y. S., Yeleğen, M. Ö., Dabanlı, İ., and Şişman, E., (2014b) Solar Irradiation Estimations and Comparisons by ANFIS, Angström-Prescott and Dependency Models, Solar Energy, 109:118-124.
  • Güçlü, Y. S., Dabanlı, İ., Şişman, E., and Şen, Z. (2015). HARmonic–LINear (HarLin) model for solar irradiation estimation. Renewable Energy, 81, 209-218.
  • Güçlü Y.S., Dabanlı İ., Şişman E., and Şen Z. (2018). Improving of the Angström-Prescott Model Using Harmonic Analysis, Exergy for A Better Environment and Improved Sustainability 2, DOI 10.1007/978-3-319-62575-1_43, Springer, Cham.
  • Hinrichsen, K. (1994) The Angström formula with coefficients having a physical meaning, Solar Energy, 52:491–495.
  • Jang, J.S.R. (1992) Self-learning fuzzy controller based on temporal back-propagation. IEEETrans Neural Networks, 3 (5) 714-723.
  • Korachagaon, I., Bapat, V.N. (2012) General formula for the estimation of global solar radiation on earth’s surface around the globe, Renewable Energy, 41:394–400.
  • Lewis, G. (1989) The Utility of the Angström –Type Equation for the Estimation of Global Radiation, Solar Energy, 43(5):297-299.
  • Lia, H., Maa, W., Liana, Y., Wanga, and X., Zhaob, L. (2011) Global solar radiation estimation with sunshine duration in Tibet, China, Renewable Energy, 36(11):3141–3145.
  • Ögelman, H., Ecevit A., and Taşemiroğlu, E. (1984) Method for estimating solar radiation from bright sunshine data, Solar Energy, 33:619–625.
  • Page, J. K. (1964) The estimation of monthly ea values of daily total short wave radiation on vertical and inclined surfaces from sunshine records for latitudes 40°N–40°S, Proceedings of the UN conference on new sources of energy, paper no. 598, 4, pp 378–390.
  • Prescott J. A. (1940) Evaporation from a water surface in relation to solar radiation. Trans. Roy. Soc. S. A. 64: 114-18.
  • Rahimikhoob, A. (2013) Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment, Renewable Energy, 35(9):2131–2135.
  • Rietveld, M. R. (1978) A new method for estimating the regression coefficients in the Formula relating solar radiation to sunshine, Agric. Meteorol., 19:243–252.
  • Ross T. J. (1995) Fuzzy logic with engineering applications. New York: McGraw Hill Co.
  • Sabbagh, J. A., Sayigh, A. A. M., and El-Salam, E. M. A. (1977) Estimation of the total solar radiation from meteorological data, Solar Energy, 19(3): 307-311.
  • Samuel T. D. M. A. (1991) Estimation of Global Radiation for Sri Lanka, Solar Energy, 47(5):333-337.
  • Soler, A. (1990) Monthly specific Rietveld’s Correlations. Solar and Wind Technology, 2/3, 305-306.
  • Sugeno, M. (1985) Industrial Applications of Fuzzy Control North-Holland, New York.
  • Swartman, R. K., and Ogunlade, O. (1967) Solar Radiation Estimates from Common Parameters, Solar Energy, 11, 170-172.
  • Şahin, A. D., (2007) A new formulation for solar irradiation and sunshine duration estimation, International Journal of Energy Research, 31:109–118.
  • Şahin, A. D., Kadioğlu, M., and Şen, Z. (2001) Monthly clearness index values of Turkey by harmonic analysis approach, Energy Conversion and Management 42:933-940.
  • Şahin, A. D., and Şen, Z. (1998) Statistical analysis of the Angström formula coefficients and application for Turkey, Solar Energy, 62:29–38.
  • Şen, Z. (2001) Angström equation parameter estimation by unrestricted method, Solar Energy, 71:95–107.
  • Şen., Z. (2002) “İstatistik Veri İşleme Yöntemleri” (In Turkish), Turkish Water Foundation Publications, pp.243, Istanbul.
  • Şen, Z. (2004) Yapay Sinir Ağları İlkeleri, Turkish Water Foundation Publications, Istanbul.
  • Şen, Z. (2007) Simple nonlinear solar irradiation estimation model, Renewable Energy, 32:342–350.
  • Şen, Z. (2008) “Solar energy fundamentals and modeling techniques”, Springer, London.
  • Şen, Z. (2017). Probabilistic innovative solar irradiation estimation. International Journal of Energy Research, 41(2), 229-239.
  • Takagi, T., and Sugeno, M. (1995) Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, 15, 1, 116-132.
  • Tolabi, H. B., Moradi, M.H. and Ayob, S. B. M., (2014) A review on classification and comparison of different models in solar radiation estimation, International Journal of Energy Research, 38:689–701.
  • Ulgen, K. and Hepbasli, A. (2002) Estimation of solar radiation parameters for Izmir, Turkey, International Journal of Energy Research, 26:807–823.
  • Wolfram Alpha, Computational Knowledge Engine, https://www.wolframalpha.com/, ziyaret tarihi: 10 Ocak 2018.
  • Wu, J., Chan, C. K., Zhang, Y., Xiong, B. Y., Zhang Q. H. (2014), Prediction of solar radiation with genetic approach combing multi-model framework, Renewable Energy, 66:132-139.

Hybrid Model for Solar Irradiation Estimation Using Polynomial and Angström-Prescott Equation

Year 2019, , 75 - 88, 01.03.2019
https://doi.org/10.15317/Scitech.2019.183

Abstract

The
world’s most important energy source, solar energy, is being investigated in a
variety of areas under different fields. Especially since the decline of fossil
fuel resources, the importance of the solar energy has increased even more.
Initial researches on solar energy started in the first quarter of the 20th
century and solar irradiation was estimated versus sunshine duration. This
study suggests similar procedure to harmonic analysis application to solar
irradiation and sunshine duration data. Basis of the methodology is combined
application of the POlynomial and classical LINear regression analysis.
Therefore, it is referred to PoLin model as a hybrid model. It isolates first
the periodicity from the daily values, and then linear regression analysis is
applied first to order stationary data. PoLin results are compared with the
classical
Angström-Prescott,
HarLin, and ANFIS models. In the application, solar irradiation site is
considered from solar energy potential location in Turkey, namely, at Diyarbakır.
Estimations by PoLin model appears more successful than
ANFIS, HarLin and Angström-Prescott approaches.

References

  • Akınoğlu B.G., and Ecevit, A. (1990) Construction of a quadratic model using modified Angström coefficients to estimate global solar radiation, Solar Energy, 45:85–92.
  • Almorox, J., Bocco, M., and Willington, E. (2013) Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina, Renewable Energy, 60:382-387.
  • Amato U., Andretta A., Banoli B., Coluzzi B., Cuomo V., Fontana F., Serio C., (1986) Markov processes and Fourier analysis as a tool describe and simulate Daily solar irradiance. Solar Energy 37(3):179-94.
  • Angström, A. (1924) Solar Terrestrial Radiation, Quarterly Journal of the Royal Meteorological Society, 50:121–126.
  • Baldasano J.M., Clar J, Berna A., (1988) Fourier analaysis of Daily solar radiation data in Spain. Solar Energy 41(4):327-34.
  • Balling R., Cerveny R.S., (1983) Spatial and temporal variations in long-term normal percent possible solar radiation levels in the United States. J Climate Appl Met 22:1726-1732.
  • Benghanem M., Mellit A., and Alamri S.N., (2009) ANN-based modelling and estimation of daily global solar radiation data: A case study, Energy Conversion and Management, 50:1644–1655.
  • Chen, S.X., Gooi, H.B., and Wang M.Q. (2013), Solar radiation forecast based on fuzzy logic and neural networks, Renewable Energy, 60, 195–201.
  • Dogniaux, R., and Lemoine, M. (1983) Classification of radiation sites in terms of different indices of atmospheric transparency. In Palz W. (éditeur), Solar Energy R&D in the European Community, Series F, Vol. 2, Solar Energy Data. D. Reidel Publ. Co., Dordecht, 94-107.
  • Gopinathan, K. K., (1988) A general formula for computing the coefficients of the correlation connecting global solar radiation to sunshine duration, Solar Energy, 41:499-502.
  • Güçlü, Y. S., Dabanlı, İ., and Şişman, E., (2014a) Short- and Long-Term Solar Radiation Estimation Method, Progress in Exergy, Energy, and the Environment, DOI 10.1007/978-3-319-04681-5_48, Springer, Cham.
  • Güçlü, Y. S., Yeleğen, M. Ö., Dabanlı, İ., and Şişman, E., (2014b) Solar Irradiation Estimations and Comparisons by ANFIS, Angström-Prescott and Dependency Models, Solar Energy, 109:118-124.
  • Güçlü, Y. S., Dabanlı, İ., Şişman, E., and Şen, Z. (2015). HARmonic–LINear (HarLin) model for solar irradiation estimation. Renewable Energy, 81, 209-218.
  • Güçlü Y.S., Dabanlı İ., Şişman E., and Şen Z. (2018). Improving of the Angström-Prescott Model Using Harmonic Analysis, Exergy for A Better Environment and Improved Sustainability 2, DOI 10.1007/978-3-319-62575-1_43, Springer, Cham.
  • Hinrichsen, K. (1994) The Angström formula with coefficients having a physical meaning, Solar Energy, 52:491–495.
  • Jang, J.S.R. (1992) Self-learning fuzzy controller based on temporal back-propagation. IEEETrans Neural Networks, 3 (5) 714-723.
  • Korachagaon, I., Bapat, V.N. (2012) General formula for the estimation of global solar radiation on earth’s surface around the globe, Renewable Energy, 41:394–400.
  • Lewis, G. (1989) The Utility of the Angström –Type Equation for the Estimation of Global Radiation, Solar Energy, 43(5):297-299.
  • Lia, H., Maa, W., Liana, Y., Wanga, and X., Zhaob, L. (2011) Global solar radiation estimation with sunshine duration in Tibet, China, Renewable Energy, 36(11):3141–3145.
  • Ögelman, H., Ecevit A., and Taşemiroğlu, E. (1984) Method for estimating solar radiation from bright sunshine data, Solar Energy, 33:619–625.
  • Page, J. K. (1964) The estimation of monthly ea values of daily total short wave radiation on vertical and inclined surfaces from sunshine records for latitudes 40°N–40°S, Proceedings of the UN conference on new sources of energy, paper no. 598, 4, pp 378–390.
  • Prescott J. A. (1940) Evaporation from a water surface in relation to solar radiation. Trans. Roy. Soc. S. A. 64: 114-18.
  • Rahimikhoob, A. (2013) Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment, Renewable Energy, 35(9):2131–2135.
  • Rietveld, M. R. (1978) A new method for estimating the regression coefficients in the Formula relating solar radiation to sunshine, Agric. Meteorol., 19:243–252.
  • Ross T. J. (1995) Fuzzy logic with engineering applications. New York: McGraw Hill Co.
  • Sabbagh, J. A., Sayigh, A. A. M., and El-Salam, E. M. A. (1977) Estimation of the total solar radiation from meteorological data, Solar Energy, 19(3): 307-311.
  • Samuel T. D. M. A. (1991) Estimation of Global Radiation for Sri Lanka, Solar Energy, 47(5):333-337.
  • Soler, A. (1990) Monthly specific Rietveld’s Correlations. Solar and Wind Technology, 2/3, 305-306.
  • Sugeno, M. (1985) Industrial Applications of Fuzzy Control North-Holland, New York.
  • Swartman, R. K., and Ogunlade, O. (1967) Solar Radiation Estimates from Common Parameters, Solar Energy, 11, 170-172.
  • Şahin, A. D., (2007) A new formulation for solar irradiation and sunshine duration estimation, International Journal of Energy Research, 31:109–118.
  • Şahin, A. D., Kadioğlu, M., and Şen, Z. (2001) Monthly clearness index values of Turkey by harmonic analysis approach, Energy Conversion and Management 42:933-940.
  • Şahin, A. D., and Şen, Z. (1998) Statistical analysis of the Angström formula coefficients and application for Turkey, Solar Energy, 62:29–38.
  • Şen, Z. (2001) Angström equation parameter estimation by unrestricted method, Solar Energy, 71:95–107.
  • Şen., Z. (2002) “İstatistik Veri İşleme Yöntemleri” (In Turkish), Turkish Water Foundation Publications, pp.243, Istanbul.
  • Şen, Z. (2004) Yapay Sinir Ağları İlkeleri, Turkish Water Foundation Publications, Istanbul.
  • Şen, Z. (2007) Simple nonlinear solar irradiation estimation model, Renewable Energy, 32:342–350.
  • Şen, Z. (2008) “Solar energy fundamentals and modeling techniques”, Springer, London.
  • Şen, Z. (2017). Probabilistic innovative solar irradiation estimation. International Journal of Energy Research, 41(2), 229-239.
  • Takagi, T., and Sugeno, M. (1995) Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, 15, 1, 116-132.
  • Tolabi, H. B., Moradi, M.H. and Ayob, S. B. M., (2014) A review on classification and comparison of different models in solar radiation estimation, International Journal of Energy Research, 38:689–701.
  • Ulgen, K. and Hepbasli, A. (2002) Estimation of solar radiation parameters for Izmir, Turkey, International Journal of Energy Research, 26:807–823.
  • Wolfram Alpha, Computational Knowledge Engine, https://www.wolframalpha.com/, ziyaret tarihi: 10 Ocak 2018.
  • Wu, J., Chan, C. K., Zhang, Y., Xiong, B. Y., Zhang Q. H. (2014), Prediction of solar radiation with genetic approach combing multi-model framework, Renewable Energy, 66:132-139.
There are 44 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Yavuz Selim Güçlü

Publication Date March 1, 2019
Published in Issue Year 2019

Cite

APA Güçlü, Y. S. (2019). ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 7(1), 75-88. https://doi.org/10.15317/Scitech.2019.183
AMA Güçlü YS. ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI. sujest. March 2019;7(1):75-88. doi:10.15317/Scitech.2019.183
Chicago Güçlü, Yavuz Selim. “ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7, no. 1 (March 2019): 75-88. https://doi.org/10.15317/Scitech.2019.183.
EndNote Güçlü YS (March 1, 2019) ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7 1 75–88.
IEEE Y. S. Güçlü, “ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI”, sujest, vol. 7, no. 1, pp. 75–88, 2019, doi: 10.15317/Scitech.2019.183.
ISNAD Güçlü, Yavuz Selim. “ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7/1 (March 2019), 75-88. https://doi.org/10.15317/Scitech.2019.183.
JAMA Güçlü YS. ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI. sujest. 2019;7:75–88.
MLA Güçlü, Yavuz Selim. “ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, vol. 7, no. 1, 2019, pp. 75-88, doi:10.15317/Scitech.2019.183.
Vancouver Güçlü YS. ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI. sujest. 2019;7(1):75-88.

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