Global Güneş Radyasyon Tahmini: Gaziantep Uygulaması

Abstract

Bu çalışmada, Gaziantep ili merkez meteorolojik gözlem istasyonundan elde edilen 2000-2010 yılları arasındaki kısıtlı periyoda ait 11 yıllık güneş  radyasyonu ve meteorolojik değişkenler (ortalama sıcaklık, bağıl nem, güneşli gün sayısı, rüzgar hızı, buhar basıncı) arasındaki ilişki genetik programlama tekniği ile formüle edilerek tahmin edilmeye çalışılmıştır. Elde edilen sonuçlar ölçülmüş veriler ile istatistiksel olarak ve trend karakteristiklerine göre karşılaştırılmıştır. Yapılan model karşılaştırmaları sonucunda mevcut veriler ile en iyi tahmin sonuçlarını lineer genetik programlama tekniğinin verdiği tespit edilmiştir. Elde edilen genetik programlama denklemleri kullanılarak türetilen sentetik verilerin güneş enerjisi santralleri projelerinin fizibilite ve tasarımında faydalı veri olarak kullanılabileceği düşünülmektedir.

Keywords

• Global güneş radyasyonu,genetik programlama,trend analizi,LOWESS

Estimation of Global Solar Radiation: A Case Study of Gaziantep

Abstract

In this study, the relationship between the global solar radiation and the meteorological variables (average temperature, relative humidity, wind velocity and vapor pressure) for limited data from 2000 to 2010 years obtained from the Gaziantep central meteorological observation station is estimated by formulating using genetics programming technique. The prediction results are compared with the observed ones statistically, and also based on their trend characteristics. The model comparison efforts revealed that linear genetic programming gave the best prediction results. It is believed that the derived genetic programming formulations can successfully be used in feasibility and design stage of solar power plants.

Keywords

• Global solar radiation,genetic programming,trend analysis,LOWESS

References

1. Engel-Cox, J. A., Nair, N.L., Ford, J.L., Evaluation of solar and meteorological data relevant to solar energy technology performance in Malaysia, Journal of Sustainable Energy&Environment, 3, 115-124, 2012.
2. Traore, S., Güven, A., New algebraic formulations of evapotranspiration extracted from Gene-Expression Programming in the tropical seasonally dry regions of West Africa, J. Irrig. Sci., 31(1), 1-10, 2013.
3. Güven, A., Kişi, O., Daily pan evaporation modeling using linear genetic programming technique, Irrig. Sci, 29(2), 135-145, 2011.
4. Güven, A., Kişi, O., Estimation of suspended sediment yield in natural rivers using machine-coded linear genetic programming, Wat. Resour. Manag., 25(2), 691-704, 2011.
5. Kişi, O., Güven, A, A machine-code based genetic programming for suspended sediment concentration estimation , Advances in Engineering Software, 41(7-8), 939-945, 2010.
6. Brameier, M., On linear genetic programming. Ph.D. thesis, University of Dortmund, 2004.
7. Nordin, J.P., A Compiling Genetic Programming System that Directly Manipulates the Machine Code. In Advances in Genetic Programming, K. Kinnear, Jr. (ed.), MIT Press, Cambridge MA, 1994.
8. Nordin, J.P., Francone, F., Banzhaf, W., Efficient Evolution of Machine Code for CISC Architectures Using Blocks and Homologous Crossover. In Advances in Genetic Programming 3, MIT Press, Cambridge MA., 1998.

9. Ferreira, C., Gene expression programming in problem solving. In: Proceedings of the 6th online world conference on soft computing in industrial applications (invited tutorial), 2001a.
10. Ferreira, Gene expression programming: a new adaptive algorithm for solving problems, Complex Syst., 13(2), 87–129, 2001b.
11. Akaike, H., Information theory and an extension of the maximum likelihood principle, Proc., 2nd Int. Symp. on Information Theory, B. N. Petrov and F. Csaki, eds., Academiai Kiado, Budapest, Hungary, 267–281, 1973.
12. Hersel, D.R., Hirsch, R.M., Statistical Methods in Water Resources. Studies in Environmental Science 49, New York: Elsevier, 1992.