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Modelling and forecasting Turkish residential electricity demand

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  • Dilaver, Zafer
  • Hunt, Lester C

Abstract

This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being -0.09 and -0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80Â TWh by 2020 compared to 40Â TWh in 2008.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Policy.

Volume (Year): 39 (2011)
Issue (Month): 6 (June)
Pages: 3117-3127

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Handle: RePEc:eee:enepol:v:39:y:2011:i:6:p:3117-3127

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Web page: http://www.elsevier.com/locate/enpol

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Keywords: Turkish residential electricity demand Structural time series model (STSM) Energy demand modelling and future scenarios;

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  1. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-89, October.
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  3. Lester C. Hunt & Yasushi Ninomiya, 2003. "Unravelling Trends and Seasonality: A Structural Time Series Analysis of Transport Oil Demand in the UK and Japan," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 63-96.
  4. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
  5. Harvey, A C, et al, 1986. "Stochastic Trends in Dynamic Regression Models: An Application to the Employment-Output Equations," Economic Journal, Royal Economic Society, vol. 96(384), pages 975-85, December.
  6. Andrew C Harvey & Andrew Scott, 1994. "Seasonality in Dynamic Regression Models," CEP Discussion Papers dp0184, Centre for Economic Performance, LSE.
  7. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
  8. Lester C. Hunt & Guy Judge & Yashushi Ninomiya, 2000. "Modelling Technical Progress: An Application of the Stochastic Trend Model to UK Energy Demand," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 99, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
  9. Halicioglu, Ferda, 2007. "Residential electricity demand dynamics in Turkey," Energy Economics, Elsevier, vol. 29(2), pages 199-210, March.
  10. John Dimitropoulos & Lester C. Hunt & Guy Judge, 2004. "Estimating Underlying Energy Demand Trends using UK Annual Data," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 108, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
  11. Rao, B. Bhaskara, 2007. "Deterministic and stochastic trends in the time series models: A guide for the applied economist," MPRA Paper 3580, University Library of Munich, Germany.
  12. Jalles, Joao Tovar, 2009. "Structural Time Series Models and the Kalman Filter: a concise review," FEUNL Working Paper Series wp541, Universidade Nova de Lisboa, Faculdade de Economia.
  13. Hamzacebi, Coskun, 2007. "Forecasting of Turkey's net electricity energy consumption on sectoral bases," Energy Policy, Elsevier, vol. 35(3), pages 2009-2016, March.
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Citations

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Cited by:
  1. Zafer Dilaver & Lester C Hunt, 2011. "Turkish Aggregate Electricity Demand: An Outlook to 2020," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 132, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
  2. Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, Open Access Journal, vol. 5(3), pages 577-598, March.
  3. Herrerias, M.J., 2013. "Seasonal anomalies in electricity intensity across Chinese regions," Applied Energy, Elsevier, vol. 112(C), pages 1548-1557.
  4. Rentizelas, Athanasios & Georgakellos, Dimitrios, 2014. "Incorporating life cycle external cost in optimization of the electricity generation mix," Energy Policy, Elsevier, vol. 65(C), pages 134-149.
  5. Zhou, Shaojie & Teng, Fei, 2013. "Estimation of urban residential electricity demand in China using household survey data," Energy Policy, Elsevier, vol. 61(C), pages 394-402.
  6. Theodoridou, Ifigeneia & Karteris, Marinos & Mallinis, Georgios & Papadopoulos, Agis M. & Hegger, Manfred, 2012. "Assessment of retrofitting measures and solar systems' potential in urban areas using Geographical Information Systems: Application to a Mediterranean city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6239-6261.

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