Eskişehir' de konutsal doğal gaz talebine ekonomik göstergelerin ve dış ortam sıcaklığının etkileri

Bu makalede, konutlarda kullanılan doğal gazın ısıtma dönemine ait aylardaki tüketiminin tahmin edilmesi için geliştirilen otoregresif zaman serisi modelleri tanıtılmaktadır. Doğal gaz tüketimiyle, zaman ve derece günlerle ifade edilen hava değişkenleri arasındaki dinamik ilişkiler araştırılmakta, ayrıca doğal gaz fiyatı, dolar satış, kuru ve tüketici fiyat endekslerini kapsayan çeşitli ekonomik göstergelerin doğal gaz kullanımına olan etkisi analiz edilmektedir. Modeller, Eskişehir de konutlarda kullanılan doğal gaza ait gözlem verileri kullanılarak oluşturulmuştur. Elde edilen sonuçlar, zaman ve hava değişkenlerinin yanında tüketicilere yönelik ekonomik göstergelerin de konutlardaki doğal gaz talebi üzerinde belirleyici bir rol oynadığını göstermektedir.

This paper describes autoregressive time series models that were designed to forecast monthly demand of natural gas for heating period in residences. Dynamic relationships have been investigated between natural gas consumption and weather variables expressed in terms of time and degree-days. Besides, the impacts of various economic indicators such as the price of natural gas, dollar exchange rate and consumer price index on natural gas consumption have been analyzed. The models have been developed by using observation data on residential natural gas usage in Eskişehir. The results have revealed that in addition to time and weather variables, economic indicators also play a significant role in the residential consumption of natural gas.

___

1. Campo, R, Ruiz, P. Adaptive Weather Sensitive Short Term Load Forecast. IEEE Transactions on Power Systems 1987; 2 (3): 592-600. 2. Hagan, MT, Behr, SM. The Time Series Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems 1987; 2 (3): 785-791. 3. Rahman, S, Bhatnagar R. An Expert System Based Algorithm For Short Term Load Forecast. IEEE Transactions on Power Systems 1988; 3 (2): 392-399. 4. Papalexopoulos, AD, Hesterberg, TC. A Regression Based Approach to Short Term System Load Forecasting. IEEE Transactions on Power Systems 1990; 5 (4): 1535-1544. 5. Charleson, LR, Weber JE. Energy forecasts for Western Australia 1992 - 2010. Energy Economics 1993; 15 (2): 111-122. 6. Hubele, NF, Cheng, CS. Identification of Seasonal Short Term Forecasting Models Using Statistical Decision Functions. IEEE Transactions on Power Systems 1990; 5 (1): 40-45. 7. Lee, RS, Singh, N. Patterns in Residential Gas and Electricity Demand : An Econometric Analysis. Journal of Business and Economic Statistics 1994; 12: 233-241. 8. Hill, T, O'Connor, M. and Remus, W. Neural Network Models for Time Series Forecasts. Management Science 1996; 42 (7): 1082-1092. 9. Connor, JT. A Robust Neural Network Filter For Electricity Demand Prediction. Journal of Forecasting 1996; 15 : 437-458. 10. Bartels, R, Fiebig, DG. Residential end-use Electricity Demand: Results From a Designed Experiment. Energy Journal 2000; 21 (2): 51-81. 11. Bohi, DR. Analyzing Demand Behavior : a Study of Energy Elasticities. Baltimore: John Hopkins Univ. Press,1981. 12. Hartman, RS. Frontiers in Energy Demand Modeling. Annual Review of Energy 1979; 4: 433-466. 13. Liu K, Subbarayan, S and Shoults, RR. Comparison of Very Short-Term Load Forecasting Techniques. IEEE Transactions on Power Systems 1996; 11 (2): 877-882. 14. Taylor, JW, Majithia, S. Using Combined Forecasts With Changing Weights For Electricity Demand Profiling. Journal of the Operational Research Society 2000; 51: 72-82. 15. Herbert, F. An Analysis of Monthly Sales of Natural Gas To Residential Customers in the United States. Energy System and Policy 1987; 10 (2): 127-147. 16. Liu, LM, Lin, MW. Forecasting Residential Consumption of Natural Gas Using Monthly ve Quarterly Time Series. International Journal of Forecasting 1991; 7: 3-16. 17. Eltony, MN. Demand for natural gas in Kuwait: An Empirical Analysis Using Two Econometric Models. International Journal of Energy Research 1996; 20 (11): 957-963. 18. Smith, P, Husein, S and Leonard, DT. Forecasting Short Term Regional Gas Demand Using an Expert System. Expert Systems with Applications 1996; 10 (2): 265-273. 19. Bartels, R, Fiebig, DG and Nahm, D. Regional end use Gas Demand in Australia. The Economic Record 1996; 72 (219): 319-331. 20. Hobbs, BF, Helman, U and Jitprapaikulsarn, S. Artificial Neural Networks for Short Term Energy Forecasting :Accuracy and Economic Value. Neurocomputing 1998; 23: 71-84. 21. Brown, RH. Development of Artificial Neural Networking Models to Predict Daily Gas Consumption. Am. Gas Assoc. Forecasting Rev. 1996; 5: 1-22. 22. Knowles, TW, Wirick, JP. The Peoples Gas Light and Coke Company Plans Gas Supply. Interfaces 1998; 28 (5): 1-12. 23. Durmayaz, A, Kadıoğlu, M and Şen, Z. An Application of the Degree-Hours Method to Estimate the Residential Heating Energy Requirement and Fuel Consumption in İstanbul. Energy 2000; 25: 1245-1256. 24. Gümrah, F, Katırcıoğlu, D, Aykan, Y, Okumuş, S and Kılınçer, N. Modeling of Gas Demand Using Degree Day Concept: Case Study for Ankara. Energy Sources 2001; 23:101-114. 25. Dağsöz, AK. Türkiye'de Derece Gün Sayıları, Ulusal Enerji Tasarruf Politikası Yapılarda Isı Yalıtımı. İstanbul, 1995. 26. Mendenhall, W, Sincich, TA. Second Course in Statistics: Regression Analysis. New Jersey: Prentice Hall Inc,1996. 27. Aras, H, Aras, N. Forecasting Residential Natural Gas Demand. Energy Sources 2004; 26: 463-472