Monthly Natural Gas Consumption’s Modelling and Its Trend Analysis For Yozgat In Turkey

Abstract

In this study an energy consumption modelling for long term (December 2006- March 2016) forecasting of monthly natural gas consumption in households and industry area for Yozgat city, Turkey was presented. In this context, it can be said that this paper has two purposes. One of them is the application and accuracy of the artificial neural networks. Estimate performances are compared with each other, and the estimates of the optimal models are evaluated with the monthly recorded natural gas consumption according to root mean square error, mean absolute error, and correlation coefficient. The other purpose of the study is to analysis trend of monthly natural gas consumption of Yozgat by using Mann-Kendall and a new method recently proposed by Şen. The results showed that the artificial neural networks gave satisfactory results in estimating monthly natural gas consumption. In the trend analysis, it was seen that both Mann-Kendall and Şen trend tests gave statistically significant increasing trend at 95% confidence level for monthly natural gas consumption of Yozgat.

Keywords

• Artificial neural networks,monthly natural gas consumption,Mann-Kendall trend test,Şen trend test,Yozgat

YOZGAT İLİ İÇİN AYLIK DOĞAL GAZ TÜKETİMİNİN MODELLENMESİ VE EĞİLİM ANALİZİ

Abstract

Bu çalışmada, Türkiye'deki Yozgat ilinde ev ve sanayide aylık doğal gaz tüketiminin uzun vadeli (Aralık 2006-Mart 2016) tahmini için bir enerji tüketimi modeli oluşturulmuştur. Bu bağlamda, bu çalışmanın iki amacı olduğu söylenebilir. Bunlardan biri yapay sinir ağlarının uygulanması ve doğruluğudur. Modellerin tahmin performansları birbirleriyle karşılaştırılır ve en uygun modellerin tahminleri, ortalama karesel hatanın karekökü, ortalama mutlak hata ve korelasyon katsayısına göre aylık olarak kaydedilen doğal gaz tüketimi ile değerlendirilir. Çalışmanın diğer amacı, Mann-Kendall eğilim testini kullanarak Yozgat'ın aylık doğal gaz tüketiminin eğilimleri ve yakın zamanda Şen tarafından önerilen yeni bir yöntemi analiz etmektir. Çalışma sonuçları, yapay sinir ağlarının aylık doğal gaz tüketiminin tahmininde tatmin edici sonuçlar verdiğini gösterdi. Eğilim analizinde, hem Mann-Kendall hem de Şen eğilim testlerinin, Yozgat'ın aylık doğal gaz tüketimi için % 95 güven düzeyinde istatistiksel olarak önemli bir artış eğilimi görüldü.

Keywords

• Yapay sinir ağları,Aylık doğal gaz tüketimi,Mann-Kendall eğilim testi,Şen eğilim testi,Yozgat

References

1. Aras, H. and Aras, N. (2004). Forecasting residential natural gas demand. Energy Sources, 26, 463-472, DOI: 10.1080/00908310490429740.
2. Ay, M. (2016a). Trend analysis of discharge at East Mediterranean River Basin in Turkey. 01st International Black Sea Congress on Environmental Sciences (01st IBCESS), Abstract Book, 12p, Giresun University, 31 August-03 September, Giresun, Turkey.
3. Ay, M. (2016b). Water Structures, Water Resources and Features of Yozgat Province in Turkey (Yozgat ili su yapıları, kaynakları ve özellikleri). I.Uluslararası Bozok Sempozyumu, Abstract Book, 294-295p, Bozok University, 05-07 May 2016, Yozgat/Turkey.
4. Ay, M. (2017). Yozgat’ın hidroklimatolojik değişkenlerinin analizi (Anaylsis of Hydroclimatologic Variables of Yozgat in Turkey). IX. Ulusal Hidroloji Kongresi, Bildiri Özetleri Kitabı, ISBN: 978-605-030-479-4, 124s., 04-06 Ekim 2017, Dicle Üniversitesi (DÜ), Diyarbakır, Türkiye.
5. Ay, M. and Kişi, Ö. (2016). Debi ve sediment değişkenlerinin trend analizi. Mühendislik Dergisi Dicle Üniversitesi, Cilt: 7, Sayı: 2, 169-180. 03-09 Temmuz 2016 (Özel Sayı). ISSN: 1309-8640. (08-10 Ekim 2015, Harran Üniversitesi, VIII. Ulusal Hidroloji Kongresi’nde Şanlıurfa’da sunulan bildiri özel sayıda yayımlanmıştır).
6. Ay, M. and Özyıldırım, S. (2017). Trend analysis of monthly total rainfall and monthly mean air temperature variables of Yozgat in Turkey. Çukurova University Journal of the Faculty of Engineering and Architecture, 32(2), 65-75, ISSN 1019-1011.
7. Ay, M. ve Kişi, Ö. (2017). Kızılırmak Nehrinde Bazı İstasyonlardaki Akımların Trend Analizi. İnşaat Mühendisleri Odası (İMO) Teknik Dergi, ACE 2014 Konferansı Özel Sayısı, 28(2), 7779-7794. (Yazı numarası 473, ISSN: 1300-3453). DOI: 10.18400/tekderg.304034.
8. Bayazit, M. and Onoz, B. (2007). To prewhiten or not to prewhiten in trend analysis? Hydrological Sciences Journal, 52(4), 611-624, DOI: 10.1623/hysj.52.4.611.

9. Brabec, M., Konár, O., Pelikán, E., Malý, M. (2008). A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers. International Journal of Forecasting, 24, 659-678, DOI: 10.1016/j.ijforecast.2008.08.005.
10. Capik, M., Kolaylı, H., Yılmaz, A.O. (2013). A comparative study on the energy demand of Turkey: coal or natural gas. Energy Exploration & Exploitation, 31(1), 119-138. DOI: 10.1260/0144-5987.31.1.119.
11. Demirbas, A. (2006). Energy priorities and new energy strategies. Energy Education Science and Technology, 16, 53-109.
12. Demirel, O.F., Zaim, S.¸ Caliskan, A., Ozuyar, P. (2012). Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods. Turkish Journal of Electrical Engineering&Computer Sciences, 20(5), DOI:10.3906/elk-1101-1029.
13. Douglas, E.M., Vogel, R.M., Kroll, C.N. (2000). Trends in floods and low flows in the United States: Impact of spatial correlation. Journal of Hydrology, 240(1-2), 90-105, DOI: 10.1016/S0022-1694(00)00336-X.
14. Ediger, V.S. and Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701-1708, DOI: 10.1016/j.enpol.2006.05.009. Energy Market Regulatory Authority (EMRA), (2017). Turkish Natural Gas Market Report 2016, 185p. Ankara 2017.
15. Erdogdu, E. (2010a). Natural gas demand in Turkey. Applied Energy, 87, 211-219, DOI: 10.1016/j.apenergy.2009.07.006.
16. Erdogdu, E. (2010b). A review of Turkish natural gas distribution market. Renewable and Sustainable Energy Reviews, 14, 806-813, DOI: 10.1016/j.rser.2009.10.023.
17. Goncu, A. (2013). An ARMA model for natural gas consumption. 3rd International Conference on Energy and Environmental Science IPCBEE, 54, IACSIT Press, Singapore DOI: 10.7763/IPCBEE. V54. 6.
18. Gumrah, F., Katircioglu, D., Aykan, Y., Okumus, S., Kilincer, N. (2001). Modeling of gas demand using degree-day concept: Case Study for Ankara. Energy Sources, 23, 101-114, DOI: 10.1080/00908310151092254.
19. Hacisalihoglu, B. (2008). Turkey’s natural gas policy. Energy Policy, 38, 1867-1872, DOI: 10.1016/j.enpol.2008.02.001.
20. Haldenbilen, S. and Ceylan, H. (2005). Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy, 33(1),89-98, DOI:10.1016/S0301-4215(03)00202-7.
21. Haykin, S. (1998). Neural Networks: A comprehensive foundation, second edition. Prentice-Hall, Upper Saddle River, NJ, pp. 26-32.
22. Helsel, D.R. and Hirsch, R.M. (2002). Statistical methods in water resources. Techniques of Water-Resources Investigations of the United States Geological Survey Book 4, Chapter A3, Hydrologic Analysis and Interpretation.
23. Kendall, M.G. (1975). Rank correlation methods. Oxford University Press, New York.
24. Kilic, A.M. (2006). Turkey’s natural gas necessity, consumption and future perspectives. Energy Policy, 34, 1928-1934, DOI: /10.1016/j.enpol.2005.02.004.
25. Kisi, O. and Ay, M. (2014). Comparison of Mann-Kendall and innovative trend method for water quality parameters of the Kizilirmak River, Turkey. Journal of Hydrology, 513, 362-375, DOI: 10.1016/j.jhydrol.2014.03.005.
26. Kottegoda, N.T. (1980). Stochastic water resources technology. The MacMillan Press.
27. Liu, L.M. and Lin, M.W. (1991). Forecasting residential consumption of natural gas using monthly and quarterly time series. International Journal of Forecasting, 07, 03-16, DOI: 10.1016/0169-2070(91)90028-T.
28. Mann, H.B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245-259, DOI: 10.2307/1907187.
29. Marquardt, D.W. (1963). An algorithm for least squares estimation of non-linear parameters. Journal of the Society Industrial and Applied Mathematics, 11, 431-441, DOI: 10.1137/0111030.
30. Melikoğlu, M. (2013). Vision 2023: Forecasting Turkey’s natural gas demand between 2013 and 2030. Renewable and Sustainable Energy Reviews, 22, 393-400, DOI: 10.1016/j.rser.2013.01.048.
31. Ministry of Energy and Natural Resources (MENR) (Turkey), (2018). General Directorate of Renewable Energy (06/12/2017 and E. 33751). National Energy Efficiency Action Plan for 2017-2023 (November 2017, Ankara). Official newspaper. 02 January 2018, 30289.
32. Ozturk, H.K. and Hepbasli, A. (2003). The place of natural gas in Turkey’s energy sources and future perspectives. Energy Sources, 25 (4), 293-307, DOI:10.1080/00908310390142334.
33. Sabo, K., Scitovski, R., Vazler, I., Zekić-Sušac, M. (2011). Mathematical models of natural gas consumption. Energy Conversion and Management, 52(3), 1721-1727, DOI: 10.1016/j.enconman.2010.10.037.
34. Sang, Y-F., Wang, Z., Liu, C. (2014). Comparison of the MK test and EMD method for trend identification in hydrological time series. Journal of Hydrology, 510, 293-298, DOI: 10.1016/j.jhydrol.2013.12.039.
35. Sarak, H. and Satman, A. (2003). The degree-day method to estimate the residential heating natural gas consumption in Turkey: A case study. Energy, 28(9), 929-939, DOI: 10.1016/S0360-5442(03)00035-5.
36. Şen, Z. (2012). Innovative trend analysis methodology. Journal of Hydrologic Engineering, 17(9), 1042-1046, DOI: 10.1061/(ASCE)HE.1943-5584.0000556.
37. Şen, Z. (2014). Trend identification simulation and application. Journal of Hydrologic Engineering, 19(3), 635-642, DOI: 10.1061/(ASCE)HE.1943-5584.0000811.
38. Şen, Z. (2015). Innovative trend significance test and applications. Theoretical and Applied Climatology, 127, 939-947, DOI: 10.1007/s00704-015-1681-x.
39. Soldo, B. (2012). Forecasting natural gas consumption. Applied Energy, 92, 26-37, DOI: 10.1016/j.apenergy.2011.11.003. The Ministry of Energy and Natural Resources (MENR). 2008. (In Turkish).
40. Toksari, M. (2010). Predicting the natural gas demand based on economic indicators: Case of Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32(6), 559-566, DOI: 10.1080/15567030802578823.
41. Vitullo, S.R., Brown, R.H. Corliss, G.F., Marx, B.M. (2009). Mathematical models for natural gas forecasting. Canadian Applied Mathematics Quarterly(CAMQ), 17(4), 807-827.
42. von Storch, H. (1995). Misuses of statistical analysis in climate research. Analysis of Climate Variability: Applications of Statistical Techniques. H.V. Storch and A. Navarra (Editors), Springer, Berlin, pp. 11-26.
43. Vondráček, J.L., Pelikán, E., Konár, O., Čermáková, J., Eben, K., Malý, M., Brabec, M. (2008). A statistical model for the estimation of natural gas consumption. Applied Energy, 85(5), 362-370, DOI: 10.1016/j.apenergy.2007.07.004.
44. Yevjevich, V. (1972). Stochastic processes in hydrology. Water Resources Publications, 276p. Fort Collins, CO.
45. Yue, S., Pilon, P., Caradias, G. (2002). Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series. Journal of Hydrology, 259, 254-271, DOI: 10.1016/S0022-1694(01)00594-7.