Global Güneş Radyasyonun Ampirik Modellenmesi için Yeni Modellerin Geliştirilmesi ve Uşak İlinde Uygulanması

Öz

Bu çalışma ile Uşak iline yatay düzleme gelen aylık ortalama global güneş radyasyonunun tespit edilebilmesi için literatürde yer alan 12 amprik model, bu çalışma kapsamında geliştirilen 2 yeni model ve PVGIS veri tabanında yer alan SARAH ve CMSAF uydu tabanlı ile COSMO ve ERA5 yeniden analiz veri setleri karşılaştırılmıştır. Çalışma kapsamında geliştirilen yeni modeller; bölgenin sıcaklık, bulutluluk oranı ve saat açısını kullanmaktadır. Çalışma kapsamındaki verilerin karşılaştırılmasında; determinasyon katsayısı (R²), ortalama yüzde hata (MPE), sapma hatası (MBE), ortalama karekök hatası (RMSE) ve yüzde hata (IeI) parametreleri kullanılmıştır. Değerlendirmeler sonucunda Uşak ilinin global güneş radyasyonu değerlerini en başarılı tahmin eden yöntem belirlenmeye çalışılmıştır. Modellerin aylık değerlendirmesine göre; Mart-Nisan-Mayıs-Haziran, Eylül-Ekim ve Aralık aylarında 14 modelin ve uydu tabanlı sistemlerin mutlak bağıl hata değerlerinin %5'in altında olduğu belirlenmiştir. Mayıs ayı için çalışmada kullanılan 18 farklı tahmin yönteminden 16'sında en doğru tahminler gerçekleşmiştir. Ampirik modellerin ve PVGIS veri setlerinin belirleme katsayısı 0,97'nin üzerindedir. Modellerin başarısı RMSE değerlerine göre değerlendirildiğinde bu çalışma kapsamında geliştirilen; saat açısı ve maks- min sıcaklık farkını kullanan logaritmik tabanlı Model 14’ün (0.90058 RMSE, 0.98327 R2, -1.079894 MPE, -0.05033 MBE ve 0.185628 t) en doğru tahminleri yaptığı belirlenmiştir. Konumsal verilerden COSMO yeniden analiz verisi (1.053134 RMSE, 0.979036 R2, -1.196348 MPE, -0.25105 MBE ve 0.8141 t) başarılı tahminler gerçekleştirmiş fakat COSMO verilerinin doğruluk seviyesi Model 14 tarafından tahmin edilen verilerden daha düşük gerçekleşmiştir. Çalışma ile kullanılan modellerin ve uydu tabanlı sistemlerin genel olarak başarılı olduğu sonucuna varılmıştır. Sonuç olarak Uşak ilinin global güneş radyasyonu tahmini için gerçekleştirilecek hassas hesaplamalarda çalışma kapsamında geliştirilen Model 14’ün kullanılabileceği, daha yüzeysel ya da ön fizibilite çalışmalarında PVGIS içerisinde yer alan COSMO yeniden analiz verisinin kullanılabileceği sonucuna varılmıştır.

Anahtar Kelimeler

• Güneş enerjisi
• modelleme
• PVGIS
• Uşak (Turkey).
• küresel güneş radyasyonu

Development of New Models Using Empirical Modeling of Global Solar Radiation and Its Application in Usak City, Turkey

Öz

With this study, 12 empirical models in the literature, 2 new models developed within the scope of this study, SARAH and CMSAF satellite-based models, COSMO and ERA5 re-analysis solar radiation data sets in the PVGIS database were compared in order to detect the monthly average global solar radiation coming to the horizontal plane of Usak province. New models developed within the scope of the study; it uses the region's temperature, cloudiness coefficient and sunset hour angle. In comparison of the datas within the scope of the study; coefficient of determination (R²), mean percent error (MPE), deviation error (MBE), root mean square error (RMSE), absolute relative error (ARE) parameters were used. As a result of the evaluations, the method that most successfully predicts the global solar radiation values of Usak province was tried to be determined. According to the monthly evaluation of the models; It was determined that 14 models and satellite-based systems have absolute relative error values below 5% in March-April-May-June, September-October and December. The most accurate estimates were realized for May in 16 of 18 different estimation methods used in the study. The coefficient of determination of empirical models and PVGIS data sets was above 0.97. When the success of the models was evaluated according to the RMSE values, It was determined that the logarithmic based Model 14 (0.90058 RMSE, 0.98327 R2, -1.079894 MPE, -0.05033 MBE, 0.185628 t) which was obtained by using the sunset hour angle and the max-min temperature difference developed within the scope of this study, made the most accurate estimations. COSMO data from spatial data (1.053134 RMSE, 0.979036 R2, -1.196348 MPE, -0.25105 MBE, 0.8141 t) made successful estimations, but the accuracy of the COSMO data was lower than the data estimated by Model 14. It was concluded that used the models and satellite-based systems were generally successful. As a result, In the studies to be carried out for the global solar radiation forecast of Usak province. It has been concluded that Model 14 developed within the scope of the study can be used in precise calculations and COSMO data from PVGIS datas can be used in more superficial or pre-feasibility studies.

Anahtar Kelimeler

• Solar energy
• global solar radiation
• modelling
• PVGIS
• Usak (Turkey).

Kaynakça

1. Almorox, J. and Hontoria, C. (2004). Global solar radiation estimation using sunshine duration in Spain. Energy Conversion and Management, 45: 1529–35.
2. 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.
3. Ampratwum, D. B. and Dorvlo, A. S. S. (1999). Estimation of solar radiation from the number of sunshine hours. Applied Energy, 63: 161–7.
4. Anacan, R. M., Cabautan, A. C., Cayabyab, J. M. A., Miguel, S. X. A., Modrigo, V. D., Rosites, C. J. V. and Sagun, A. C. (2018). Development of oil quality estimator using machine vision system. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) 29 November - 02 December, P. 1-6, Baguio City, Philippines.
5. Angstrom, A. (1924). Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quarterly Journal of the Royal Meteorological Society, 50(210): 121-126.
6. Anonymous (2020a). Pre-Investment feasibility of electricity generation from solar energy in Usak industry. https://investinusak.gov.tr/assets/upload/dosyalar/usak-sanayisinde-gunes-enerjisinden-elektrik-uretiminin-yatirim-oncesi-fizibilitesi.pdf/ (Accessed Date: 24.04.2020).
7. Anonymous (2020b). Land use of Usak province. Prime Ministry, Republic of Turkey, General Directorate of Rural Services Publications, Report No: 64, Ankara, Turkey.
8. Anonymous (2020c). Climate data of Usak for long years. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=USAK/ (Accessed Date: 17.12.2020).

9. Anonymous (2021a). Solar energy map of Usak province, https://www.enerjiatlasi.com/gunes-enerjisi-haritasi/usak/ (Accessed Date: 04.01.2021).
10. Anonymous (2021b). PVGIS users manuel. https://ec.europa.eu/jrc/en/PVGIS/docs/usermanual/ (Accessed Date: 09.01.2021).
11. Anonymous (2021c). Numerical Analysis. https://web.karabuk.edu.tr/yasinortakci/dokumanlar/say%C4%B1sal_analiz/turkce/2.pdf/ (Accessed Date: 01.03.2021).
12. Artkın, F. (2018). The renewable energy sources and technologies of potential in Turkey. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 4: 285-295.
13. Bahel, V., Bakhsh, H. and Srinivasan, R. (1987). A correlation for estimation of global solar radiation. Energy, 12: 131–5.
14. Bristow, K. L. and Campbell, G. (1984). On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31(2): 159-166.
15. Cooper, P. I. (1969). The absorption of solar radiation in solar stills. Solar Energy, 12: 3.
16. Coppolino, S. (1994). A new correlation between clearness index and relative sunshine. Renewable Energy, 4(4): 417-423.
17. Dogniaux, R. and Lemoine, M. (1983). Classification of radiation sites in terms of different indices of atmospheric transparency. Solar energy research and development in the European Community, Dordrecht, Holland: Reidel, Series F, Dordrecht, Holland: Reidel; 2:94–107.
18. Duffie, J. A. and Beckman, W. A. (1980). Solar Engineering of Thermal Processes. 4th Edition, Wiley, New York, U.S.A.
19. Dyer, S. A. and Dyer, J. S. (2007). Approximations to error functions. IEEE Instrumentation & Measurement Magazine, 10(6): 45-48.
20. Elagib, N. and Mansell, M. (2000). New approaches for estimating global solar radiation across Sudan. Energy Conversion and Management, 41(5): 419-434.
21. El-Metwally, M. (2005). Sunshine and global solar radiation estimation at different sites in Egypt. Journal of Atmospheric and Solar-Terrestrial Physics, 67(14): 1331–1342.
22. Feng, Y., Chen, D. and Zhao, X. (2019). Improved empirical models for estimating surface direct and diffuse solar radiation at monthly and daily level: A case study in North China. Progress in Physical Geography: Earth and Environment, 43(1): 80-94.
23. Frank, C. W., Wahl, S., Keller, J. D., Pospichal, B., Hense, A. and Crewell, S. (2018). Bias correction of a novel European reanalysis data set for solar energy applications. Solar Energy, 164: 12–24.
24. Green, K. and Tashman, L. (2009). Percentage error: What denominator?. Foresight: The International Journal of Applied Forecasting, , International Institute of Forecasters 12: 36-40.
25. Güçlü, Y. S. (2019). Hybrid model for solar irradiation estimation using polynomial and Angström-Prescott equation. Selcuk University Journal of Engineering, Science and Technology, 7(1): 75-88 (In Turkish).
26. Gül, M. and Çelik, E. (2017). Global solar radiation estimation for Tunceli province using ANFIS. Dicle University Faculty of Engineering, Engineering Journal, 8(4): 891-899 (In Turkish).
27. Hargreaves, G. L., Hargreaves, G. H. and Riley, J. P. (1985). Irrigation water requirements for Senegal River basin. Journal of Irrigation and Drainage Engineering, 111(3): 265-275.
28. Ineichen, P. (2014). Long term satellite global, beam and diffuse irradiance validation. Energy Procedia, 48: 1586–1596.
29. Işık, E. and İnallı, M. (2011). Global radiation predict by using artificial neural network for Tunceli city. Journal of New World Sciences Academy, Engineering Sciences, 6(1): 190-194.
30. Kallioğlu, M. A., Karakaya, H. and Avci, A. S. (2015). Analysis of sunshine hours and global solar radiation for Mardin of Turkey. 3rd International Symposium on Innovative Technologies in Engineering and Science, Universitat Politecnica de Valencia, 3-5 June, Valencia, Spain.
31. Kimball, H. H. (1919). Variations in the total and luminous solar radiation with geographical position in the United States. Monthly Weather Review, 47(11), 769-793.
32. Klein, S. A. (1977). Calculation of monthly average insolation on tilted surfaces. Solar Energy, 19: 325.
33. Kulcu, R., Suslu, A., Cihanalp, C. and Yilmaz, D. (2017). Modelling of global solar radiation on horizontal surfaces for Mersin city. Wind Energy, 433(216): 127-4.
34. Külcü R. (2015). Modelling of solar radiation reaching the earth to Isparta province. Süleyman Demirel University, Journal of The Faculty of Agriculture, 10(1): 19-26.
35. Külcü R. (2019). Development of a new model using empirical modeling of global solar radiation and its application in Çankırı city. Yekarum, 4(2): 1-8 (In Turkish).
36. Külcü, R. and Ersan, R. (2021). Empirical modelling of global solar radiation in Hatay (Turkey) province. Journal of Tekirdag Agricultural Faculty, 18(3): 446 - 456 (In Turkish).
37. Marfo, P. and Okyere, G. A. (2019). The accuracy of effect-size estimates under normals and contaminated normals in meta-analysis. Heliyon, 5(6): e01838.
38. Navarro M. C. (1992). Small area estimation: estimating selected economic statistics for provinces of the Philippines. (MSc. Thesis) Statistics, Australian National University, Canberra, Australia.
39. Önler, E. and Kayışoğlu, B. (2023). Estimation of monthly, seasonal and annual total solar radiation on the tilted surface at optimum tilt angles in two provinces, Turkiye. Journal of Tekirdag Agricultural Faculty, 20(3): 712-722.
40. Prescott J. (1940). Evaporation from a water surface in relation to solar radiation. Transactions of the Royal Society of South Australia. 64(1): 114-118.
41. Psiloglou, B. E., Kambezidis, H. D., Kaskaoutis, D. G., Karagiannis, D. and Polo, J. M. (2020). Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece. Renewable energy, 146: 1372-1391.
42. Skeiker, K. (2006). Correlation of global solar radiation with common geographical and meteorological parameters for Damascus province, Syria. Energy Conversion and Management, 47(4): 331-345. Supit, I. and Van Kappel, R. R. (1998). A simple method to estimate global radiation. Solar Energy, 63(3): 147-160.
43. Tırmıkçı, C.A. (2018). Real time comparison of a two axis solar tracking system and an optimally tilted fixed solar System for yearly adjustment. (Ph.D. Thesis) Sakarya University, Institute of Natural Sciences, Electrical and Electronics Engineering Department, Sakarya, Turkey.
44. Urraca, R., Gracia-Amillo, A. M., Huld, T., Martinez-de-Pison, F. J., Trentmann, J., Lindfors, A. V., Riihela, A and Sanz-Garcia, A. (2017). Quality control of global solar radiation data with satellite-based products. Solar Energy, 158: 49-62.
45. Urraca, R., Huld, T., Gracia-Amillo, A., Martinez-de-Pison, F. J., Kaspar, F. and Sanz-Garcia, A. (2018). Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data. Solar Energy, 164: 339–354.