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Modified Gravitational Search Algorithm for Energy Demand Estimation of Turkey

Year 2019, Volume: 8 Issue: 4, 1338 - 1348, 24.12.2019
https://doi.org/10.17798/bitlisfen.527899

Abstract

Estimation
of energy demand beforehand is a quite significant problem in respect of
economy and sources of country. In this study, Gravitational Search Algorithm
(GSA) was modified by making some innovations in GSA and called as Modified
Gravitational Search Algorithm (MGSA). Energy demand estimation is conducted
through the relationship between the increase in economic indicators in Turkey
and energy consumption. Estimation was actualized by using gross domestic
product (GSYH), importation, exportation and demography for energy demand
estimation and both linear and exponential equations. Energy demand between the
years 2017-2037 was predicted by using the data belong to 1997-2011. The years
between 2012 and 2016 were used as test data. It was observed that the results
acquired via MGSA estimate better compared to GSA results.

References

  • [1] M. Beskirli, H. Hakli, and H. Kodaz, "The energy demand estimation for Turkey using differential evolution algorithm," Sādhanā, vol. 42, pp. 1705-1715, 2017.
  • [2] I. Dincer and S. Dost, "Energy intensities for Canada," Applied Energy, vol. 53, pp. 283-298, 1996.
  • [3] E. Aktaş and O. Alioğlu, "Türkiye’de enerji sektörü analizi: Marmara bölgesi termik santraller örneği," Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 21, 2012.
  • [4] M. D. Toksarı, "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, vol. 35, pp. 3984-3990, 2007.
  • [5] V. Ş. Ediger and S. Akar, "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, vol. 35, pp. 1701-1708, 2007.
  • [6] V. Ş. Ediger and H. Tatlıdil, "Forecasting the primary energy demand in Turkey and analysis of cyclic patterns," Energy Conversion and Management, vol. 43, pp. 473-487, 2002.
  • [7] Z. Yumurtaci and E. Asmaz, "Electric energy demand of Turkey for the year 2050," Energy Sources, vol. 26, pp. 1157-1164, 2004.
  • [8] M. Akkurt, O. F. Demirel, and S. Zaim, "Forecasting Turkey’s natural gas consumption by using time series methods," European Journal of Economic and Political Studies, vol. 3, pp. 1-21, 2016.
  • [9] M. Mucuk and D. Uysal, "Turkey’s energy demand," Current Research Journal of Social Sciences, vol. 1, pp. 123-128, 2009.
  • [10] Z. Dilaver and L. C. Hunt, "Industrial electricity demand for Turkey: a structural time series analysis," Energy Economics, vol. 33, pp. 426-436, 2011.
  • [11] A. Sözen and E. Arcaklioğlu, "Prospects for future projections of the basic energy sources in Turkey," Energy Sources, Part B, vol. 2, pp. 183-201, 2007.
  • [12] M. Kankal, A. Akpınar, M. İ. Kömürcü, and T. Ş. Özşahin, "Modeling and Forecasting of Turkey’s Energy Consumption Using Socio-economic and Demographic Variables," Applied Energy, vol. 88, pp. 1927-1939, 2011.
  • [13] A. Sozen, E. Arcaklioglu, and M. Ozkaymak, "Modelling of Turkey's net energy consumption using artificial neural network," International Journal of Computer Applications in Technology, vol. 22, pp. 130-136, 2005.
  • [14] H. Ceylan and H. K. Ozturk, "Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach," Energy Conversion and Management, vol. 45, pp. 2525-2537, 2004.
  • [15] S. Haldenbilen and H. Ceylan, "Genetic algorithm approach to estimate transport energy demand in Turkey," Energy Policy, vol. 33, pp. 89-98, 2005.
  • [16] M. S. Kıran, E. Özceylan, M. Gündüz, and T. Paksoy, "A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey," Energy conversion and management, vol. 53, pp. 75-83, 2012.
  • [17] A. Ünler, "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025," Energy Policy, vol. 36, pp. 1937-1944, 2008.
  • [18] M. S. Kıran and M. Gündüz, "A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems," Applied Soft Computing, vol. 13, pp. 2188-2203, 2013.
  • [19] H. Uguz, H. Hakli, and O. K. Baykan, "A New Algorithm Based on Artificial Bee Colony Algorithm for Energy Demand Forecasting in Turkey," in 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 2015, pp. 56-61.
  • [20] M. F. Tefek, H. Uğuz, and M. Güçyetmez, "A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey," Neural Computing and Applications, pp. 1-16, 2017.
  • [21] M. F. Tefek and U. Harun, "Estimation of Turkey Electric Energy Demand until Year 2035 Using TLBO Algorithm," International Journal of Intelligent Systems and Applications in Engineering, vol. 4, pp. 48-52, 2016.
  • [22] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: A Gravitational Search Algorithm," Information Sciences, vol. 179, pp. 2232-2248, Jun 13 2009.
  • [23] J. Xin, G. Chen, and Y. Hai, "A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight," in 2009 International Joint Conference on Computational Sciences and Optimization, 2009, pp. 505-508.
  • [24] MOD. (2018). Medium Term Programme 2018-2020. Available: http://www.mod.gov.tr/Pages/content.aspx?l=99479284-12e6-4d7d-bb4d-10d2a19feded&i=21
  • [25] TSI. (2018). Main Statistics -Population and Demography. Available: http://www.turkstat.gov.tr/UstMenu.do?metod=temelist
  • [26] TURKSTAT. (2013, 16.11.2016). Turkish Statistical Institute. Available: http://www.turkstat.gov.tr/UstMenu.do?metod=temelist

Modified Gravitational Search Algorithm for Energy Demand Estimation of Turkey

Year 2019, Volume: 8 Issue: 4, 1338 - 1348, 24.12.2019
https://doi.org/10.17798/bitlisfen.527899

Abstract

Ülke
ekonomisi ve kaynakları bakımından enerji talebini önceden tahmin etmek çok
önemli bir problemdir. Bu çalışmada, Yerçekimi Arama Algoritması (YAA) ile
YAA’da yapılan bazı yenilikler yapılarak modifiye edilmiş ve Modifiye Yerçekimi
Arama Algoritması (MYAA) olarak adlandırılmıştır. Enerji talep tahmini,
Türkiye’deki ekonomik göstergelerin artışı ile enerji tüketimi arasındaki
ilişki ile gerçekleşmektedir. Enerji talep tahmini için gayri safi yurtiçi
hasıla (GSYH), ithalat, ihracat ve nüfus bilgileri hem lineer hem de üssel
denklemler kullanılarak tahmin işlemi gerçekleştirildi. 1997-2011 yılları
arasındaki veriler kullanılarak 2017-2037 yılları arasındaki enerji talebi
tahmin edilmiştir. 2012 ile 2016 yılları ise test verisi olarak kullanılmıştır.
MGSA ile elde edilen sonuçlar GSA sonuçlarına göre daha iyi bir tahmin
gerçekleştirdiği görülmüştür.

References

  • [1] M. Beskirli, H. Hakli, and H. Kodaz, "The energy demand estimation for Turkey using differential evolution algorithm," Sādhanā, vol. 42, pp. 1705-1715, 2017.
  • [2] I. Dincer and S. Dost, "Energy intensities for Canada," Applied Energy, vol. 53, pp. 283-298, 1996.
  • [3] E. Aktaş and O. Alioğlu, "Türkiye’de enerji sektörü analizi: Marmara bölgesi termik santraller örneği," Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 21, 2012.
  • [4] M. D. Toksarı, "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, vol. 35, pp. 3984-3990, 2007.
  • [5] V. Ş. Ediger and S. Akar, "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, vol. 35, pp. 1701-1708, 2007.
  • [6] V. Ş. Ediger and H. Tatlıdil, "Forecasting the primary energy demand in Turkey and analysis of cyclic patterns," Energy Conversion and Management, vol. 43, pp. 473-487, 2002.
  • [7] Z. Yumurtaci and E. Asmaz, "Electric energy demand of Turkey for the year 2050," Energy Sources, vol. 26, pp. 1157-1164, 2004.
  • [8] M. Akkurt, O. F. Demirel, and S. Zaim, "Forecasting Turkey’s natural gas consumption by using time series methods," European Journal of Economic and Political Studies, vol. 3, pp. 1-21, 2016.
  • [9] M. Mucuk and D. Uysal, "Turkey’s energy demand," Current Research Journal of Social Sciences, vol. 1, pp. 123-128, 2009.
  • [10] Z. Dilaver and L. C. Hunt, "Industrial electricity demand for Turkey: a structural time series analysis," Energy Economics, vol. 33, pp. 426-436, 2011.
  • [11] A. Sözen and E. Arcaklioğlu, "Prospects for future projections of the basic energy sources in Turkey," Energy Sources, Part B, vol. 2, pp. 183-201, 2007.
  • [12] M. Kankal, A. Akpınar, M. İ. Kömürcü, and T. Ş. Özşahin, "Modeling and Forecasting of Turkey’s Energy Consumption Using Socio-economic and Demographic Variables," Applied Energy, vol. 88, pp. 1927-1939, 2011.
  • [13] A. Sozen, E. Arcaklioglu, and M. Ozkaymak, "Modelling of Turkey's net energy consumption using artificial neural network," International Journal of Computer Applications in Technology, vol. 22, pp. 130-136, 2005.
  • [14] H. Ceylan and H. K. Ozturk, "Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach," Energy Conversion and Management, vol. 45, pp. 2525-2537, 2004.
  • [15] S. Haldenbilen and H. Ceylan, "Genetic algorithm approach to estimate transport energy demand in Turkey," Energy Policy, vol. 33, pp. 89-98, 2005.
  • [16] M. S. Kıran, E. Özceylan, M. Gündüz, and T. Paksoy, "A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey," Energy conversion and management, vol. 53, pp. 75-83, 2012.
  • [17] A. Ünler, "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025," Energy Policy, vol. 36, pp. 1937-1944, 2008.
  • [18] M. S. Kıran and M. Gündüz, "A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems," Applied Soft Computing, vol. 13, pp. 2188-2203, 2013.
  • [19] H. Uguz, H. Hakli, and O. K. Baykan, "A New Algorithm Based on Artificial Bee Colony Algorithm for Energy Demand Forecasting in Turkey," in 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 2015, pp. 56-61.
  • [20] M. F. Tefek, H. Uğuz, and M. Güçyetmez, "A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey," Neural Computing and Applications, pp. 1-16, 2017.
  • [21] M. F. Tefek and U. Harun, "Estimation of Turkey Electric Energy Demand until Year 2035 Using TLBO Algorithm," International Journal of Intelligent Systems and Applications in Engineering, vol. 4, pp. 48-52, 2016.
  • [22] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: A Gravitational Search Algorithm," Information Sciences, vol. 179, pp. 2232-2248, Jun 13 2009.
  • [23] J. Xin, G. Chen, and Y. Hai, "A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight," in 2009 International Joint Conference on Computational Sciences and Optimization, 2009, pp. 505-508.
  • [24] MOD. (2018). Medium Term Programme 2018-2020. Available: http://www.mod.gov.tr/Pages/content.aspx?l=99479284-12e6-4d7d-bb4d-10d2a19feded&i=21
  • [25] TSI. (2018). Main Statistics -Population and Demography. Available: http://www.turkstat.gov.tr/UstMenu.do?metod=temelist
  • [26] TURKSTAT. (2013, 16.11.2016). Turkish Statistical Institute. Available: http://www.turkstat.gov.tr/UstMenu.do?metod=temelist
There are 26 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Mehmet Beşkirli 0000-0002-4842-3817

Mehmet Fatih Tefek 0000-0003-3390-4201

Harun Uğuz This is me 0000-0003-4617-202X

Publication Date December 24, 2019
Submission Date February 15, 2019
Acceptance Date September 24, 2019
Published in Issue Year 2019 Volume: 8 Issue: 4

Cite

IEEE M. Beşkirli, M. F. Tefek, and H. Uğuz, “Modified Gravitational Search Algorithm for Energy Demand Estimation of Turkey”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 8, no. 4, pp. 1338–1348, 2019, doi: 10.17798/bitlisfen.527899.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS