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Yapay Sinir Ağları ve Zaman Serileri Yöntemi ile Bir Endüstri Alanının (İvedik OSB) Elektrik Enerjisi İhtiyaç Tahmini

Year 2018, Volume: 11 Issue: 3, 255 - 261, 31.07.2018
https://doi.org/10.17671/gazibtd.404250

Abstract

Özel firmalara enerji sağlayan firmalar veya
işletmeler için enerji tüketiminin tahmini ve ihtiyaç planlaması çok kritiktir.
Özellikle endüstri bölgelerindeki enerji ihtiyacı ev kullanıcılarının
ihtiyacından daha yüksektir, bundan dolayı enerji ihtiyacının doğru tahminini
gerektirir. Bu çalışmada, zaman serileri ve yapay sinir ağları olmak üzere iki
farklı yaklaşım kullanılarak Türkiye’deki bir endüstri bölgesi için enerji
ihtiyaç tahmini üzerinde çalışılmış ve sonuçlar test edilmiştir. Daha önceki
çalışmalardan farklı olarak, kısıtlı veri ile kısa dönem tahmini için basit bir
model geliştirilmiştir. Model, giriş parametresi olarak geçmiş günlere ait
tüketim verileri ve sıcaklığı içermektedir. Sıcaklık verisi, endüstri
bölgelerinde ısıtma amaçlı enerji tüketiminde kullanıldığı için anahtar rol
oynamaktadır. Zaman serileri yaklaşımında sadece geçmişe ait enerji tüketim
verileri kullanılmıştır. Her iki yaklaşım enerji ihtiyaç tahmininde
kullanılmış, sonuçlar tartışılmış ve karşılaştırılmıştır.

References

  • H. Son, C. Kim, "Short-term forecasting of electricity demand for the residential sector using weather and social variables", Resour Conserv Recy, 123, 200-207, 2017.
  • H.K. Ozturk, H. Ceylan, "Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study", Int J Energ Res, 29(9), 829-840, 2005.
  • A. Kialashaki, J.R. Reisel, "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States", Energy, 76(Supplement C), 749-760, 2014.
  • A. Azadeh, S.F. Ghaderi, S. Sohrabkhani, "Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors", Energ Convers Manage, 49(8), 2272-2278, 2008.
  • Z.W. Geem, W.E. Roper, "Energy demand estimation of South Korea using artificial neural network", Energ Policy, 37(10), 4049-4054, 2009.
  • U. Kumar, V.K. Jain, "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India", Energy, 35(4), 1709-1716, 2010.
  • S. Mollaiy-Berneti, "Developing energy forecasting model using hybrid artificial intelligence method", Journal of Central South University, 22(8), 3026-3032, 2015.
  • S. Mollaiy-Berneti, "Optimal design of adaptive neuro-fuzzy inference system using genetic algorithm for electricity demand forecasting in Iranian industry", Soft Computing, 20(12), 4897-4906, 2016.
  • A.E. Clements, A.S. Hurn, Z. Li, "Forecasting day-ahead electricity load using a multiple equation time series approach", European Journal of Operational Research, 251(2), 522-530, 2016.
  • L.P.C. Do, K.-H. Lin, P. Molnár, "Electricity consumption modelling: A case of Germany", Economic Modelling, 55(Supplement C), 92-101, 2016.
  • C. Hamzacebi, "Forecasting of Turkey's net electricity energy consumption on sectoral bases", Energ Policy, 35(3), 2009-2016, 2007.
  • D. Akay, M. Atak, "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey", Energy, 32(9), 1670-1675, 2007.
  • M.D. Toksari, "Ant colony optimization approach to estimate energy demand of Turkey", Energ Policy, 35(8), 3984-3990, 2007.
  • A. Unler, "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025", Energ Policy, 36(6), 1937-1944, 2008.
  • A. Sozen, E. Arcaklioglu, "Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey", Energ Policy, 35(10), 4981-4992, 2007.
  • A. Sozen, E. Arcaklioglu, M. Ozkaymak, "Turkey's net energy consumption", Appl Energ, 81(2), 209-221, 2005.
  • S. Kucukali, K. Baris, "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach", Energ Policy, 38(5), 2438-2445, 2010.
  • M. Bilgili, B. Sahin, A. Yasar, E. Simsek, "Electric energy demands of Turkey in residential and industrial sectors", Renew Sust Energ Rev, 16(1), 404-414, 2012.
  • M. H. Calp, "İşletmeler için Personel Yemek Talep Miktarının Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi", Politeknik Dergisi, DOI: 10.2339/politeknik.444380, 21(4), 2018.
  • L. Suganthi, A.A. Samuel, "Energy models for demand forecasting-A review", Renew Sust Energ Rev, 16(2), 1223-1240, 2012.
Year 2018, Volume: 11 Issue: 3, 255 - 261, 31.07.2018
https://doi.org/10.17671/gazibtd.404250

Abstract

References

  • H. Son, C. Kim, "Short-term forecasting of electricity demand for the residential sector using weather and social variables", Resour Conserv Recy, 123, 200-207, 2017.
  • H.K. Ozturk, H. Ceylan, "Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study", Int J Energ Res, 29(9), 829-840, 2005.
  • A. Kialashaki, J.R. Reisel, "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States", Energy, 76(Supplement C), 749-760, 2014.
  • A. Azadeh, S.F. Ghaderi, S. Sohrabkhani, "Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors", Energ Convers Manage, 49(8), 2272-2278, 2008.
  • Z.W. Geem, W.E. Roper, "Energy demand estimation of South Korea using artificial neural network", Energ Policy, 37(10), 4049-4054, 2009.
  • U. Kumar, V.K. Jain, "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India", Energy, 35(4), 1709-1716, 2010.
  • S. Mollaiy-Berneti, "Developing energy forecasting model using hybrid artificial intelligence method", Journal of Central South University, 22(8), 3026-3032, 2015.
  • S. Mollaiy-Berneti, "Optimal design of adaptive neuro-fuzzy inference system using genetic algorithm for electricity demand forecasting in Iranian industry", Soft Computing, 20(12), 4897-4906, 2016.
  • A.E. Clements, A.S. Hurn, Z. Li, "Forecasting day-ahead electricity load using a multiple equation time series approach", European Journal of Operational Research, 251(2), 522-530, 2016.
  • L.P.C. Do, K.-H. Lin, P. Molnár, "Electricity consumption modelling: A case of Germany", Economic Modelling, 55(Supplement C), 92-101, 2016.
  • C. Hamzacebi, "Forecasting of Turkey's net electricity energy consumption on sectoral bases", Energ Policy, 35(3), 2009-2016, 2007.
  • D. Akay, M. Atak, "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey", Energy, 32(9), 1670-1675, 2007.
  • M.D. Toksari, "Ant colony optimization approach to estimate energy demand of Turkey", Energ Policy, 35(8), 3984-3990, 2007.
  • A. Unler, "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025", Energ Policy, 36(6), 1937-1944, 2008.
  • A. Sozen, E. Arcaklioglu, "Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey", Energ Policy, 35(10), 4981-4992, 2007.
  • A. Sozen, E. Arcaklioglu, M. Ozkaymak, "Turkey's net energy consumption", Appl Energ, 81(2), 209-221, 2005.
  • S. Kucukali, K. Baris, "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach", Energ Policy, 38(5), 2438-2445, 2010.
  • M. Bilgili, B. Sahin, A. Yasar, E. Simsek, "Electric energy demands of Turkey in residential and industrial sectors", Renew Sust Energ Rev, 16(1), 404-414, 2012.
  • M. H. Calp, "İşletmeler için Personel Yemek Talep Miktarının Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi", Politeknik Dergisi, DOI: 10.2339/politeknik.444380, 21(4), 2018.
  • L. Suganthi, A.A. Samuel, "Energy models for demand forecasting-A review", Renew Sust Energ Rev, 16(2), 1223-1240, 2012.
There are 20 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Semih Özden 0000-0002-1309-9391

Ali Öztürk

Publication Date July 31, 2018
Submission Date March 12, 2018
Published in Issue Year 2018 Volume: 11 Issue: 3

Cite

APA Özden, S., & Öztürk, A. (2018). Yapay Sinir Ağları ve Zaman Serileri Yöntemi ile Bir Endüstri Alanının (İvedik OSB) Elektrik Enerjisi İhtiyaç Tahmini. Bilişim Teknolojileri Dergisi, 11(3), 255-261. https://doi.org/10.17671/gazibtd.404250