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Forecasting the energy demand of Turkey with a NN based on an improved Particle Swarm Optimization

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Abstract

Forecasting the future energy demand accurately is a critical issue, especially for countries like Turkey where the energy dependency ratio is high. This paper presents a neural network based on the particle swarm optimization algorithm with mutation (PSOM-NN) to enhance the prediction accuracy of the energy demand of Turkey. Unlike some studies in the field which are using all the observed data for training purpose, the proposed network used only a part of these data for training. Approximately 63 % and 37 % of the mentioned data are used for the training and test, respectively. Detrending is applied to the data to avoid nonlinear transfer functions that constrain the model to the input range values. The analysis of the results revealed that PSOM-NN produced better forecasts of energy demand compared to the earlier studies in terms of root-mean-square error, mean absolute percentage error and mean absolute deviation. Finally, future projections under different scenarios are also employed and discussed. It is believed that the proposed model could be applied to any country that needs accurate forecasts of the energy demand for sustainable energy policies.

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References

  1. International Energy Agency (2013). http://www.iea.org/newsroomandevents/pressreleases/2010/july/name,20287,en.html. Accessed 23 July 2013

  2. Republic of Turkey Prime Ministry (2010) Investment Support and Development Agency of Turkey & Deloitte, Turkish Energy Industry Report

  3. European Commission (2013) Eurostat. http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/The_EU_in_the_world_-_energy#Energy_imports. Accessed 24 April 2013

  4. Suganthi L, Samuel Anand A (2012) Energy models for demand forecasting—a review. Renew Sustain Energy Rev 16(2):1223–1240

    Article  Google Scholar 

  5. Farahbakhsh H, Ugursal VI, Fung AS (1998) A residential end-use energy consumption model for Canada. Int J Energy Res 22(13):1133–1143

    Article  Google Scholar 

  6. Chavez S Gonzales, Xiberta Bernat J, Llaneza Coalla H (1999) Forecasting of energy production and consumption in Asturias (Northern Spain). Energy 24(3):183–198

    Article  Google Scholar 

  7. Ediger VŞ, Tatlıdil H (2002) Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Convers Manag 43(4):473–487

    Article  Google Scholar 

  8. Yumurtacı Z, Asmaz E (2004) Electric energy demand of Turkey for the year 2050. Energy Sources 26:1157–1164

    Article  Google Scholar 

  9. Ediger VS, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35(3):1701–1708

    Article  Google Scholar 

  10. Kankal M, Akpınar A, Kömürcü Mİ, Özşahin TŞ (2011) Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Appl Energy 88(5):1927–1939

    Article  Google Scholar 

  11. Aydin G (2014) Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections. Renew Sustain Energy Rev 35:382–389

    Article  Google Scholar 

  12. Ceylan H, Öztürk HK (2004) Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Convers Manag 45:2525–2537

    Article  Google Scholar 

  13. Canyurt OE, Ceylan H, Ozturk HK, Hepbasli A (2004) Energy demand estimation based on two-different genetic algorithm approaches. Energy Sources 26(14):1313–1320

    Article  Google Scholar 

  14. Haldenbilen S, Ceylan H (2005) Genetic Algorithm Approach to Estimate Transport Energy Demand in Turkey. Energy Policy 33:89–98

    Article  Google Scholar 

  15. Toksarı MD (2007) Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy 35(8):3984–3990

    Article  Google Scholar 

  16. Ünler A (2008) Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy 36(6):1937–1944

    Article  Google Scholar 

  17. Kıran MS, Özceylan E, Gündüz M, Paksoy T (2010) A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Convers Manag 53(1):75–83

    Article  Google Scholar 

  18. Özçelik Y, Hepbaşlı A (2006) Estimating petroleum exergy production and consumption using a simulated annealing approach. Energy Sources B: Econ Plan Policy 1(3):255–265

    Article  Google Scholar 

  19. El-Telbany M, El-Karmi F (2008) Short-term forecasting of Jordanian electricity demand using particle swarm optimization. Electr Power Syst Res 78(3):425–433

    Article  Google Scholar 

  20. Chakravarty S, Dash PK (2011) Dynamic filter weights neural network model integrated with differential evolution for day-ahead price forecasting in energy market. Expert Syst Appl 38:10974–10982

    Article  Google Scholar 

  21. Görücü FB, Geriş PU, Gümrah F (2004) Artificial neural network modeling for forecasting gas consumption. Energy Sources 26:299–307

    Article  Google Scholar 

  22. Sözen A, Arcaklıoğlu E, Özkaymak M (2005) Turkey’s net energy consumption. Appl Energy 81(2):209–221

    Article  Google Scholar 

  23. Sözen A, Akçayol MA, Arcaklıoğlu E (2006) Forecasting net energy consumption using artificial neural network. Energy Sources, Part B: Econ, Plann Policy 1:147–155

    Article  Google Scholar 

  24. Sözen A, Arcaklıoğlu E (2007) Prediction of Net Energy Consumption Based on Economic Indicators (GNP and GDP) in Turkey. Energy Policy 35:4981–4992

    Article  Google Scholar 

  25. Murat YS, Ceylan H (2006) Use of Artificial Neural Networks for Transport Energy Demand Modeling. Energy Policy 34:3165–3172

    Article  Google Scholar 

  26. Hamzaçebi C (2007) Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy 35(3):2009–2016

    Article  Google Scholar 

  27. Kavaklıoğlu K, Ceylan H, Öztürk HK, Canyurt OE (2009) Modeling and Prediction of Turkey’s Electricity Consumption Using Artificial Neural Networks. Energy Convers Manag 50:2719–2727

    Article  Google Scholar 

  28. Sözen A, Arcaklioğlu E (2007) Prospects for future projections of the basic energy sources in Turkey. Energy Sources B 2(2):183–201

    Article  Google Scholar 

  29. Sözen A (2009) Future projection of the energy dependency of Turkey using artificial neural network. Energy Policy 37(11):4827–4833

    Article  Google Scholar 

  30. Dibike YB, Solomatine DP (2001) River flow forecasting using artificial neural networks. Phys Chem Earth Part B 26(1):1–7

    Article  Google Scholar 

  31. Geem ZW, Roper WE (2009) Energy demand estimation of South Korea using artificial neural network. Energy Policy 37(10):4049–4054

    Article  Google Scholar 

  32. Pao HT (2009) Forecasting energy consumption in Taiwan using hybrid nonlinear models. Energy 34(10):1438–1446

    Article  Google Scholar 

  33. Ekonomou L (2010) Greek long tern energy consumption prediction using artificial neural networks. Energy 35:512–517

    Article  Google Scholar 

  34. Shiwei Yu, Zhu Kejun, Zhang Xian (2012) Energy demand projection of China using a path-coefficient analysis and PSO–GA approach. Energy Convers Manag 53(1):142–153

    Article  Google Scholar 

  35. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: The state of the art. Int J Forecast 14:35–62

    Article  Google Scholar 

  36. Bashir ZA, El-Hawary ME (2009) Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Trans Power Syst 24(1):20–27

    Article  Google Scholar 

  37. Dehuri S, Cho SB (2010) A comprehensive survey on functional link neural networks and an adaptive PSO-BP learning for CFLNN. Neural Comput Appl 19(2):187–205

    Article  Google Scholar 

  38. Ata R (2015) Artificial neural networks applications in wind energy systems: a review. Renew Sustain Energy Rev 49:534–556

    Article  Google Scholar 

  39. Shayeghi H, Shayanfar HA, Azimi G (2009) STLF based on optimized neural network using PSO. World Academy of Science, Engineering and Technology 3:889–899

    Google Scholar 

  40. Quaiyum S, Khan YI, Rahman S, Barman P (2011) Artificial neural network based short term load forecasting of power system. International Journal of Computer Applications 30(4):1–7

    Google Scholar 

  41. Banda E, Folly KA (2015) Short Term Load Forecasting Based on Hybrid ANN and PSO. Springer International Publishing, In Advances in Swarm and Computational Intelligence, pp 98–106

    Google Scholar 

  42. Jiang X, Ling H, Yan J, Li B, Li Z (2013) Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization. Math Probl Eng 2013:1–8. doi:10.1155/2013/194730

    Google Scholar 

  43. Ren C, An N, Wanga J, Li L, Hud B, Shang D (2014) Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowl-Based Syst 56:226–239

    Article  Google Scholar 

  44. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948

  45. Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources, vol 1. In: Proceedings of the 2001 congress on evolutionary computation, pp 81–86

  46. Daş MT, Dulger LC, Daş GS (2013) Robotic applications with particle swarm optimization (PSO). In: 2013 international conference on control, decision and information technologies (CoDIT). IEEE

  47. Gerhard Venter (2003) Sobieszczanski-Sobieski J (2003) Particle swarm optimization. AIAA journal 41(8):1583–1589

    Article  Google Scholar 

  48. Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, Berlin

    MATH  Google Scholar 

  49. Higashi N, Iba H (2003) Particle swarm optimization with gaussian mutation. In: Proceedings of the IEEE swarm intelligence symposium, pp 72–79

  50. Stacey A, Jancic M, Grundy I (2003) Particle swarm optimization with mutation. In: Proceedings of the 2003 congress on evolutionary computation. IEEE Press, pp 1425–1430

  51. Esquivel SC, Coello Coello CA (2003) On the use of particle swarm optimization with multimodal functions. In: Proceedings of the 2003 congress on evolutionary computation. IEEE Press, pp 1130–1136

  52. Zhang Q-L, Li X, Tran Q-A (2005) A modified particle swarm optimization algorithm. In: Proceedings of the fourth international conference on machine learning and cybernetics

  53. Andrews PS (2006) An investigation into mutation operators for particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 1044–1051

  54. Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Appl 18(2):135–140

    Article  Google Scholar 

  55. Sharda R (1994) Neural networks for the MS/OR analyst: An application bibliography. Interfaces 24(2):116–130

    Article  Google Scholar 

  56. Suresh Y (2015) Software fault prediction and test data generation using artificial intelligent techniques. Ph.D. thesis, Department of Computer Science and Engineering, National Institute of Technology Rourkela, India. http://ethesis.nitrkl.ac.in/6704/1/yeresime_phd_2015.pdf

  57. Turkish Ministry of Energy and Natural Resources (2013). www.enerji.gov.tr. Accessed July 2013

  58. Schumann J, Liu Y (2006) Performance estimation of a neural network-based controller. In: Wang J, Yi Z, Zurada JM, Lu B-L, Yin H (eds) Advances in neural networks-ISNN 2006. Springer, Berlin, pp 981–990

  59. Eaves Andrew HC, Kingsman BG (2004) Forecasting for the ordering and stock-holding of spare parts. J Oper Res Soc 55(4):431–437

    Article  MATH  Google Scholar 

  60. Peter Zhang G, Qi Min (2005) Neural network forecasting for seasonal and trend time series. Eur J Oper Res 160(2):501–514

    Article  MATH  Google Scholar 

  61. Lind Douglas A, Marchal William G, Wathen Samuel A (2012) Statistical techniques in business and economics, 15th edn. McGraw-Hill, New York

    Google Scholar 

  62. Taskaya-Temizel T, Casey MC (2005) A comparative study of Autoregressive Neural Network Hybrids. Neural Networks 18(5–6):781–789

    Article  Google Scholar 

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  1. Department of Industrial Engineering, Kırıkkale University, Ankara Yolu 7. Km Yahşihan, Kırıkkale, 71450, Turkey

    Gülesin Sena Daş

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Daş, G.S. Forecasting the energy demand of Turkey with a NN based on an improved Particle Swarm Optimization. Neural Comput & Applic 28 (Suppl 1), 539–549 (2017). https://doi.org/10.1007/s00521-016-2367-8

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