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Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey

Year 2018, Volume: 2 Issue: 2, 20 - 30, 31.12.2018

Abstract

This study is
conducted to get predictions for the generation of electricity by annual
production shares and decide the most suitable method for future periods.
Between 2010-2017 in Turkey, the relation of generation shares of coal, natural
gas, liquid fuels with the greenhouse gas is examined with the artificial
neural network and the regression analysis. The input “greenhouse gas” to
explain the effect in the using the energy sources for the electricity
generation is searched. Artificial neural network (ANN) and regression analysis
methods are used in the study. As a result, ANN gives better results than the
regression analysis.

References

  • [1] L. Suganthi, A. A. Samuel, Energy models for demand forecasting - A review. Renewable and Sustainable Energy Reviews 2012;16(2):1223-1240.
  • [2] M. Balat, Energy consumption and economic growth in Turkey during the past two decades. Energy Policy 2008;36(1):118–127.
  • [3] V. S. Ediger, H. Tatlidil, Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management 2002;43:473–487.
  • [4] G. Zhang, B. E. Patuwo, M. Y. Hu, Forecasting with artificial neural networks: The state of the art. Inter. Journal of Forecasting 1998;14:35-62.
  • [5] S. A. Kalogirou, Applications of artificial neural-networks for energy systems. Energy Systems 2000;17-35.
  • [6] Y. Sewsynker-Sukai , F. Faloye , E. B. G. Kana, Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnology & Biotechnological Equipment 2017;31(2):221-235.
  • [7] R, Sharda, R. B. Patil, Connectionist approach to time series prediction: An emprical test. Journal of Intelligent Manufacturing 1992;3:317-323.
  • [8] T, Hill, M, O’Connor, W. Remus, Neural networks models for time series forecasts. Management Sciences 1996;42(7):1082-1092. [9] C. Hamzacebi, Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy 2007;35(3):2009-2016.
  • [10] A. Sozen, E. Arcaklıoglu, M. Ozkaymak, Turkey’s net energy consumption. Applied Energy 2005;81(2):209-221.
  • [11] A. Sozen, E. Arcaklıoglu, Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy 2007;35(10):4981-4992.
  • [12] T. Al-Saba, I. El-Amin, Artificial neural networks as applied to long-term demand forecasting, Artificial Intelligence in Engineering 1999;13:189-197.
  • [13] G. K. F. Tso, K. K. W. Yau, Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy 2007;32(9):1761-1768.
  • [14] O, Karkacier, Z. G. Goktolga, A. Cicek, A regression analysis of the effect of energy use in agriculture. Energy Policy 2006;34:3796-3800.
  • [15] B. Kermanshahi, H. Iwamiya, Up to year 2020 load forecasting using neural nets. Electrical Power and Energy Systems 2002;24:789-797.
  • [16] Z. W. Geem, W. E. Roper, Energy demand estimation of South Korea using artificial neural network. Energy Policy 2009;37(10):4049-4054.
  • [17] M. Bessec, J. Fouquau, The non-linear link between electricity consumption and temperature in Europe: a threshold panel approach. Energy Economics 2008;30(5):2705-2721.
  • [18] O. Kaynar, I. Yilmaz, F. Demirkoparan, Forecasting of natural gas consumption with neural network and neuro fuzzy system. Energy Education Science and Technology Part A: Energy Science and Research 2011;26(2):221-238.
  • [19] J. Szoplik, Forecasting of natural gas consumption with artificial neural networks. Energy 2015;85:208-220.
  • [20] A. Khotanzad, H. Elragal, T. L. Lu, Combination of artificial neural-network forecasters for prediction of natural gas consumption. IEEE Transactions on Neural Networks 2000;11(2):464-473.
  • [21] F. B. Gorucu, P. U. Geri, S. F. Gumrah, Artificial neural network modeling for forecasting gas consumption. Energy Sources 2004;26:299-307.
  • [22] E. Assareh , M. A. Behrang , A. Ghanbarzadeh, The integration of artificial neural networks and particle swarm optimization to forecast world green energy consumption. Energy Sources Part B-Economics Planning and Policy 2012;7(4):398-410.
  • [23] K. Ermis, A. Midilli , I. Dincer , M. A. Rosen, Artificial neural network analysis of world green energy use. Energy Policy 2007;35(3):1731-1743.
  • [24] S. Akcan, Y. Kuvvetli, H. Kocyigit, Time series analysis models for estimation of greenhouse gas emitted by different sectors in Turkey. Human and ecological risk assessment 2018;24(2):522-533.
  • [25] F. Menten, B. Chèze, L. Patouillard, F. Bouvart, A review of LCA greenhouse gas emissions results for advanced biofuels: The use of meta-regression analysis. Renewable and Sustainable Energy Reviews 2013;26:108-134.
  • [26] O. A. Olanrewaju , C. Mbohwa, Assessing potential reduction in greenhouse gas: An integrated approach. Journal of Cleaner Production 2017;141:891-899.
  • [27] D. Antanasijevic , V. Pocajt , M. Ristic , A. Peric-Grujic, Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy 2015;84:816-824.
  • [28] http://www.tuik.gov.tr

Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey

Year 2018, Volume: 2 Issue: 2, 20 - 30, 31.12.2018

Abstract

This study is
conducted to get predictions for the generation of electricity by annual
production shares and decide the most suitable method for future periods.
Between 2010-2017 in Turkey, the relation of generation shares of coal, natural
gas, liquid fuels with the greenhouse gas is examined with the artificial
neural network and the regression analysis. The input “greenhouse gas” to
explain the effect in the using the energy sources for the electricity
generation is searched. Artificial neural network (ANN) and regression analysis
methods are used in the study. As a result, ANN gives better results than the
regression analysis.

References

  • [1] L. Suganthi, A. A. Samuel, Energy models for demand forecasting - A review. Renewable and Sustainable Energy Reviews 2012;16(2):1223-1240.
  • [2] M. Balat, Energy consumption and economic growth in Turkey during the past two decades. Energy Policy 2008;36(1):118–127.
  • [3] V. S. Ediger, H. Tatlidil, Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management 2002;43:473–487.
  • [4] G. Zhang, B. E. Patuwo, M. Y. Hu, Forecasting with artificial neural networks: The state of the art. Inter. Journal of Forecasting 1998;14:35-62.
  • [5] S. A. Kalogirou, Applications of artificial neural-networks for energy systems. Energy Systems 2000;17-35.
  • [6] Y. Sewsynker-Sukai , F. Faloye , E. B. G. Kana, Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnology & Biotechnological Equipment 2017;31(2):221-235.
  • [7] R, Sharda, R. B. Patil, Connectionist approach to time series prediction: An emprical test. Journal of Intelligent Manufacturing 1992;3:317-323.
  • [8] T, Hill, M, O’Connor, W. Remus, Neural networks models for time series forecasts. Management Sciences 1996;42(7):1082-1092. [9] C. Hamzacebi, Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy 2007;35(3):2009-2016.
  • [10] A. Sozen, E. Arcaklıoglu, M. Ozkaymak, Turkey’s net energy consumption. Applied Energy 2005;81(2):209-221.
  • [11] A. Sozen, E. Arcaklıoglu, Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy 2007;35(10):4981-4992.
  • [12] T. Al-Saba, I. El-Amin, Artificial neural networks as applied to long-term demand forecasting, Artificial Intelligence in Engineering 1999;13:189-197.
  • [13] G. K. F. Tso, K. K. W. Yau, Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy 2007;32(9):1761-1768.
  • [14] O, Karkacier, Z. G. Goktolga, A. Cicek, A regression analysis of the effect of energy use in agriculture. Energy Policy 2006;34:3796-3800.
  • [15] B. Kermanshahi, H. Iwamiya, Up to year 2020 load forecasting using neural nets. Electrical Power and Energy Systems 2002;24:789-797.
  • [16] Z. W. Geem, W. E. Roper, Energy demand estimation of South Korea using artificial neural network. Energy Policy 2009;37(10):4049-4054.
  • [17] M. Bessec, J. Fouquau, The non-linear link between electricity consumption and temperature in Europe: a threshold panel approach. Energy Economics 2008;30(5):2705-2721.
  • [18] O. Kaynar, I. Yilmaz, F. Demirkoparan, Forecasting of natural gas consumption with neural network and neuro fuzzy system. Energy Education Science and Technology Part A: Energy Science and Research 2011;26(2):221-238.
  • [19] J. Szoplik, Forecasting of natural gas consumption with artificial neural networks. Energy 2015;85:208-220.
  • [20] A. Khotanzad, H. Elragal, T. L. Lu, Combination of artificial neural-network forecasters for prediction of natural gas consumption. IEEE Transactions on Neural Networks 2000;11(2):464-473.
  • [21] F. B. Gorucu, P. U. Geri, S. F. Gumrah, Artificial neural network modeling for forecasting gas consumption. Energy Sources 2004;26:299-307.
  • [22] E. Assareh , M. A. Behrang , A. Ghanbarzadeh, The integration of artificial neural networks and particle swarm optimization to forecast world green energy consumption. Energy Sources Part B-Economics Planning and Policy 2012;7(4):398-410.
  • [23] K. Ermis, A. Midilli , I. Dincer , M. A. Rosen, Artificial neural network analysis of world green energy use. Energy Policy 2007;35(3):1731-1743.
  • [24] S. Akcan, Y. Kuvvetli, H. Kocyigit, Time series analysis models for estimation of greenhouse gas emitted by different sectors in Turkey. Human and ecological risk assessment 2018;24(2):522-533.
  • [25] F. Menten, B. Chèze, L. Patouillard, F. Bouvart, A review of LCA greenhouse gas emissions results for advanced biofuels: The use of meta-regression analysis. Renewable and Sustainable Energy Reviews 2013;26:108-134.
  • [26] O. A. Olanrewaju , C. Mbohwa, Assessing potential reduction in greenhouse gas: An integrated approach. Journal of Cleaner Production 2017;141:891-899.
  • [27] D. Antanasijevic , V. Pocajt , M. Ristic , A. Peric-Grujic, Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy 2015;84:816-824.
  • [28] http://www.tuik.gov.tr
There are 27 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Articles
Authors

Elifcan Göçmen

Onur Derse

Publication Date December 31, 2018
Acceptance Date September 27, 2018
Published in Issue Year 2018 Volume: 2 Issue: 2

Cite

APA Göçmen, E., & Derse, O. (2018). Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey. International Scientific and Vocational Studies Journal, 2(2), 20-30.
AMA Göçmen E, Derse O. Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey. ISVOS. December 2018;2(2):20-30.
Chicago Göçmen, Elifcan, and Onur Derse. “Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey”. International Scientific and Vocational Studies Journal 2, no. 2 (December 2018): 20-30.
EndNote Göçmen E, Derse O (December 1, 2018) Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey. International Scientific and Vocational Studies Journal 2 2 20–30.
IEEE E. Göçmen and O. Derse, “Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey”, ISVOS, vol. 2, no. 2, pp. 20–30, 2018.
ISNAD Göçmen, Elifcan - Derse, Onur. “Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey”. International Scientific and Vocational Studies Journal 2/2 (December 2018), 20-30.
JAMA Göçmen E, Derse O. Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey. ISVOS. 2018;2:20–30.
MLA Göçmen, Elifcan and Onur Derse. “Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey”. International Scientific and Vocational Studies Journal, vol. 2, no. 2, 2018, pp. 20-30.
Vancouver Göçmen E, Derse O. Forecasting of Electricity Generation Shares by Fossil Fuels Using Artificial Neural Network and Regression Analysis in Turkey. ISVOS. 2018;2(2):20-3.


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