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Comparison of Energy Consumption Prediction Methods With ANN AND ANFIS Techniques

Yıl 2019, , 1029 - 1044, 31.07.2019

Fırat Ekinci

https://doi.org/10.29130/dubited.485822
Cited By: 6

Öz

Kaynakça

  • [1] Anonim, (20.11.2018) [Online] erişim Enerji ve Tabii kaynaklar Bakanlığı(ETKB); https://www.enerji.gov.tr/tr-tr/sayfalar/elektrik www.enerji.gov.tr
  • [2] V. Ş. Ediger ve H. Tatlıdil, “Forecasting the primary energy demand in Turkey and analysis of cyclic patterns”, Energy Conversion and Management, vol. 43, no 4, pp. 473-487,. 2002
  • [3] Z. Yumurtaci ve E. Asmaz, “Electric Energy Demand of Turkey for the Year 2050”, Energy Sources, vol. 26, no 12, pp. 1157-1164, 2004.
  • [4] H. Ceylan ve H. K. Ozturk, “Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach”, Energy Conversion and Management, vol. 45, no15-16, pp. 2525-2537, 2004.
  • [5] O. Ersel Canyurt, H. Ceylan, H. Kemal Ozturk, ve A. Hepbasli, “Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches”, Energy Sources, c. 26, sy 14, ss. 1313-1320, Ara. 2004.
  • [6] H. Ceylan, H. K. Ozturk, A. Hepbasli, ve Z. Utlu, “Estimating Energy and Exergy Production and Consumption Values Using Three Different Genetic Algorithm Approaches. Part 2: Application and Scenarios”, Energy Sources, vol. 27, no 7, pp. 629-639, 2005.
  • [7] A. Sözen, E. Arcaklioğlu, ve M. Özkaymak, “Turkey’s net energy consumption”, Applied Energy, vol. 81, no 2, pp. 209-221, 2005.
  • [8] S. Haldenbilen ve H. Ceylan, “Genetic algorithm approach to estimate transport energy demand in Turkey”, Energy Policy, vol. 33, no 1, pp. 89-98, 2005.
  • [9] H. K. Ozturk, H. Ceylan, O. E. Canyurt, ve A. Hepbasli, “Electricity estimation using genetic algorithm approach: a case study of Turkey”, Energy, vol. 30, no 7, pp. 1003-1012, 2005.
  • [10] A. Sözen, M. A. Akçayol, ve E. Arcaklioğlu, “Forecasting Net Energy Consumption Using Artificial Neural Network”, Energy Sources, Part B: Economics, Planning, and Policy, vol. 1, no 2, pp. 147-155, 2006.
  • [11] V. Ş. Ediger, S. Akar, ve B. Uğurlu, “Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model”, Energy Policy, vol. 34, no 18, pp. 3836-3846, 2006.
  • [12] Y. S. Murat ve H. Ceylan, “Use of artificial neural networks for transport energy demand modeling”, Energy Policy, vol. 34, no 17, pp. 3165-3172, 2006.
  • [13] V. Ş. Ediger ve S. Akar, “ARIMA forecasting of primary energy demand by fuel in Turkey”, Energy Policy, c. 35, sy 3, ss. 1701-1708, Mar. 2007.
  • [14] M. Duran Toksarı, “Ant colony optimization approach to estimate energy demand of Turkey”, Energy Policy, vol. 35, no 8, pp. 3984-3990, 2007.
  • [15] E. Erdogdu, “Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey”, Energy Policy, vol. 35, no 2, pp. 1129-1146, 2007.
  • [16] C. Hamzaçebi, “Forecasting of Turkey’s net electricity energy consumption on sectoral bases”, Energy Policy, vol. 35, no 3, pp. 2009-2016, 2007.
  • [17] A. Sözen, “Future projection of the energy dependency of Turkey using artificial neural network”, Energy Policy, vol. 37, no 11, pp. 4827-4833, 2009.
  • [18] A. Sözen, Z. Gülseven, ve E. Arcaklioğlu, “Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies”, Energy Policy, vol. 35, no 12, pp. 6491-6505, 2007.
  • [19] A. Sözen ve E. Arcaklioglu, “Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey”, Energy Policy, vol. 35, no 10, pp. 4981-4992, 2007.
  • [20] D. Akay ve M. Atak, “Grey prediction with rolling mechanism for electricity demand forecasting of Turkey”, Energy, vol. 32, no 9, p. 1670-1675, 2007.
  • [21] A. Ünler, “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025”, Energy Policy, vol. 36, no 6, pp. 1937-1944, 2008.
  • [22] H. Ceylan, H. Ceylan, S. Haldenbilen, ve O. Baskan, “Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey”, Energy Policy, vol. 36, no 7, pp. 2527-2535, Tem. 2008.
  • [23] K. Kavaklioglu, H. Ceylan, H. K. Ozturk, ve O. E. Canyurt, “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks”, Energy Conversion and Management, vol. 50, no 11, pp. 2719-2727, 2009.
  • [24] A. Sozen ve E. Arcaklioğlu, “Prospects for Future Projections of the Basic Energy Sources in Turkey”, Energy Sources, Part B: Economics, Planning, and Policy, vol. 2, no. 2, pp. 183-201, Nis. 2007.
  • [25] M. Kankal, A. Akpınar, M. İ. Kömürcü, ve T. Ş. Özşahin, “Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables”, Applied Energy, c. 88, sy 5, ss. 1927-1939, May. 2011.
  • [26] G. Oğcu, O. F. Demirel, ve S. Zaim, “Forecasting Electricity Consumption with Neural Networks and Support Vector Regression”, Procedia - Social and Behavioral Sciences, vol. 58, pp. 1576-1585,. 2012.
  • [27] K. Kavaklioglu, “Robust electricity consumption modeling of Turkey using Singular Value Decomposition”, International Journal of Electrical Power & Energy Systems, vol. 54, pp. 268-276, 2014.
  • [28] C. Hamzacebi ve H. A. Es, “Forecasting the annual electricity consumption of Turkey using an optimized grey model”, Energy, vol. 70, pp. 165-171, 2014
  • [29] S. Tutun, C.-A. Chou, ve E. Canıyılmaz, “A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey”, Energy, vol. 93, pp. 2406-2422, 2015.
  • [30] F. Kaytez, M. C. Taplamacioglu, E. Cam, ve F. Hardalac, “Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines”, International Journal of Electrical Power & Energy Systems, vol. 67, pp. 431-438, 2015.
  • [31] M. E. Günay, “Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey”, Energy Policy, vol. 90, pp. 92-101, 2016.
  • [32] E. Yukseltan, A. Yucekaya, ve A. H. Bilge, “Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation”, Applied Energy, vol. 193, pp. 287-296, 2017.
  • [33] Y. Yaslan ve B. Bican, “Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting”, Measurement, vol. 103, pp. 52-61, 2017.
  • [34] M. Bulut ve B. Başoğlu, “Kısa Dönem Elektrik Talep Tahminleri İçin Yapay Sinir Ağları ve Uzman Sistemler Tabanlı Hibrid Tahmin Sistemi Geliştirilmesi”, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 32, sy 2, Haz. 2017.
  • [35] M. Bilgili ve M. Ozgoren, “Daily total global solar radiation modeling from several meteorological data”, Meteorology and Atmospheric Physics, vol. 112, no 3-4, pp. 125-138, May. 2011.
  • [36] Graupe, D., 2007. Principles of artificial neural networks. (2nd ed.) Chicago: World Scientific Publishing Co. Pte. Ltd.
  • [37] Jang, J.-S.R. 1993. ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Systems, Man, and Cybernetics, vol. 23, no 3, pp 665-684.
  • [38] Jang J.S.R., Sun C.T., 1995. Neuro-Fuzzy Modeling and Control, Proc. IEEE, 83, 3, 378-406.
  • [39] Jang J.S.R., Sun C.T., Mizutani E., 1997. Neuro-Fuzzy and Soft Computing. Prentice Hall.
  • [40] A. Al-Hmouz, Jun Shen, R. Al-Hmouz, ve Jun Yan, “Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning”, IEEE Transactions on Learning Technologies, vol. 5, no 3, pp. 226-237, Tem. 2012.

YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması

Yıl 2019, , 1029 - 1044, 31.07.2019

Fırat Ekinci

https://doi.org/10.29130/dubited.485822
Cited By: 6

Öz

Enerji tüketimi, ülkelerin sosyal
ve ekonomik gelişmişliğini gösteren en önemli faktörlerden biridir. Son
yıllarda,  Türkiye'nin enerji talebi de yaşanan
ekonomik ve sosyal büyüme ile birlikte artmaktadır. Artan enerji talebinin
planlanması ve yönetilmesi ülkenin enerji politikası için hayati öneme
sahiptir. Enerjinin planlaması ve yönetiminin doğru ve güvenilir olması
ekonomik ve doğal kaynakların etkin kullanılmasını sağlayacaktır. Enerji
planlanması ve yönetiminde tahmin yöntemleri ve algoritmalar enerji talebinin
belirlenmesinde kullanılan en yaygın yöntemlerdir. Elektrik enerjisi talebi
bölgesel, mevsimsel ve anlık dalgalanmalar gösterebilmektedir. Bu nedenle,
tahmine etki eden faktörlerin doğru belirlenmesi ve değerlendirilmesi gerekir.
Yaşanan ekonomik büyüme birlikte artan konut ihtiyacı da enerji talebini
artırmaktadır. Bu çalışmada, 1970-2015 yıllarına ait Türkiye elektrik enerjisi
verileri işlenmiş ve Yapay Sinir Ağları (YSA) ve Adaptif Ağ Tabanlı Bulanık
Mantık Çıkarım Sistemi (ANFIS) yöntemleri ile yapılan tahminler gerçekleşen
tüketim değerleri karşılaştırılarak iki yöntemin performans analizi
yapılmıştır.

Anahtar Kelimeler

Enerji tahmini Yapay Sinir Ağı Enerji tüketimi

Kaynakça

  • [1] Anonim, (20.11.2018) [Online] erişim Enerji ve Tabii kaynaklar Bakanlığı(ETKB); https://www.enerji.gov.tr/tr-tr/sayfalar/elektrik www.enerji.gov.tr
  • [2] V. Ş. Ediger ve H. Tatlıdil, “Forecasting the primary energy demand in Turkey and analysis of cyclic patterns”, Energy Conversion and Management, vol. 43, no 4, pp. 473-487,. 2002
  • [3] Z. Yumurtaci ve E. Asmaz, “Electric Energy Demand of Turkey for the Year 2050”, Energy Sources, vol. 26, no 12, pp. 1157-1164, 2004.
  • [4] H. Ceylan ve H. K. Ozturk, “Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach”, Energy Conversion and Management, vol. 45, no15-16, pp. 2525-2537, 2004.
  • [5] O. Ersel Canyurt, H. Ceylan, H. Kemal Ozturk, ve A. Hepbasli, “Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches”, Energy Sources, c. 26, sy 14, ss. 1313-1320, Ara. 2004.
  • [6] H. Ceylan, H. K. Ozturk, A. Hepbasli, ve Z. Utlu, “Estimating Energy and Exergy Production and Consumption Values Using Three Different Genetic Algorithm Approaches. Part 2: Application and Scenarios”, Energy Sources, vol. 27, no 7, pp. 629-639, 2005.
  • [7] A. Sözen, E. Arcaklioğlu, ve M. Özkaymak, “Turkey’s net energy consumption”, Applied Energy, vol. 81, no 2, pp. 209-221, 2005.
  • [8] S. Haldenbilen ve H. Ceylan, “Genetic algorithm approach to estimate transport energy demand in Turkey”, Energy Policy, vol. 33, no 1, pp. 89-98, 2005.
  • [9] H. K. Ozturk, H. Ceylan, O. E. Canyurt, ve A. Hepbasli, “Electricity estimation using genetic algorithm approach: a case study of Turkey”, Energy, vol. 30, no 7, pp. 1003-1012, 2005.
  • [10] A. Sözen, M. A. Akçayol, ve E. Arcaklioğlu, “Forecasting Net Energy Consumption Using Artificial Neural Network”, Energy Sources, Part B: Economics, Planning, and Policy, vol. 1, no 2, pp. 147-155, 2006.
  • [11] V. Ş. Ediger, S. Akar, ve B. Uğurlu, “Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model”, Energy Policy, vol. 34, no 18, pp. 3836-3846, 2006.
  • [12] Y. S. Murat ve H. Ceylan, “Use of artificial neural networks for transport energy demand modeling”, Energy Policy, vol. 34, no 17, pp. 3165-3172, 2006.
  • [13] V. Ş. Ediger ve S. Akar, “ARIMA forecasting of primary energy demand by fuel in Turkey”, Energy Policy, c. 35, sy 3, ss. 1701-1708, Mar. 2007.
  • [14] M. Duran Toksarı, “Ant colony optimization approach to estimate energy demand of Turkey”, Energy Policy, vol. 35, no 8, pp. 3984-3990, 2007.
  • [15] E. Erdogdu, “Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey”, Energy Policy, vol. 35, no 2, pp. 1129-1146, 2007.
  • [16] C. Hamzaçebi, “Forecasting of Turkey’s net electricity energy consumption on sectoral bases”, Energy Policy, vol. 35, no 3, pp. 2009-2016, 2007.
  • [17] A. Sözen, “Future projection of the energy dependency of Turkey using artificial neural network”, Energy Policy, vol. 37, no 11, pp. 4827-4833, 2009.
  • [18] A. Sözen, Z. Gülseven, ve E. Arcaklioğlu, “Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies”, Energy Policy, vol. 35, no 12, pp. 6491-6505, 2007.
  • [19] A. Sözen ve E. Arcaklioglu, “Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey”, Energy Policy, vol. 35, no 10, pp. 4981-4992, 2007.
  • [20] D. Akay ve M. Atak, “Grey prediction with rolling mechanism for electricity demand forecasting of Turkey”, Energy, vol. 32, no 9, p. 1670-1675, 2007.
  • [21] A. Ünler, “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025”, Energy Policy, vol. 36, no 6, pp. 1937-1944, 2008.
  • [22] H. Ceylan, H. Ceylan, S. Haldenbilen, ve O. Baskan, “Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey”, Energy Policy, vol. 36, no 7, pp. 2527-2535, Tem. 2008.
  • [23] K. Kavaklioglu, H. Ceylan, H. K. Ozturk, ve O. E. Canyurt, “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks”, Energy Conversion and Management, vol. 50, no 11, pp. 2719-2727, 2009.
  • [24] A. Sozen ve E. Arcaklioğlu, “Prospects for Future Projections of the Basic Energy Sources in Turkey”, Energy Sources, Part B: Economics, Planning, and Policy, vol. 2, no. 2, pp. 183-201, Nis. 2007.
  • [25] M. Kankal, A. Akpınar, M. İ. Kömürcü, ve T. Ş. Özşahin, “Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables”, Applied Energy, c. 88, sy 5, ss. 1927-1939, May. 2011.
  • [26] G. Oğcu, O. F. Demirel, ve S. Zaim, “Forecasting Electricity Consumption with Neural Networks and Support Vector Regression”, Procedia - Social and Behavioral Sciences, vol. 58, pp. 1576-1585,. 2012.
  • [27] K. Kavaklioglu, “Robust electricity consumption modeling of Turkey using Singular Value Decomposition”, International Journal of Electrical Power & Energy Systems, vol. 54, pp. 268-276, 2014.
  • [28] C. Hamzacebi ve H. A. Es, “Forecasting the annual electricity consumption of Turkey using an optimized grey model”, Energy, vol. 70, pp. 165-171, 2014
  • [29] S. Tutun, C.-A. Chou, ve E. Canıyılmaz, “A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey”, Energy, vol. 93, pp. 2406-2422, 2015.
  • [30] F. Kaytez, M. C. Taplamacioglu, E. Cam, ve F. Hardalac, “Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines”, International Journal of Electrical Power & Energy Systems, vol. 67, pp. 431-438, 2015.
  • [31] M. E. Günay, “Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey”, Energy Policy, vol. 90, pp. 92-101, 2016.
  • [32] E. Yukseltan, A. Yucekaya, ve A. H. Bilge, “Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation”, Applied Energy, vol. 193, pp. 287-296, 2017.
  • [33] Y. Yaslan ve B. Bican, “Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting”, Measurement, vol. 103, pp. 52-61, 2017.
  • [34] M. Bulut ve B. Başoğlu, “Kısa Dönem Elektrik Talep Tahminleri İçin Yapay Sinir Ağları ve Uzman Sistemler Tabanlı Hibrid Tahmin Sistemi Geliştirilmesi”, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 32, sy 2, Haz. 2017.
  • [35] M. Bilgili ve M. Ozgoren, “Daily total global solar radiation modeling from several meteorological data”, Meteorology and Atmospheric Physics, vol. 112, no 3-4, pp. 125-138, May. 2011.
  • [36] Graupe, D., 2007. Principles of artificial neural networks. (2nd ed.) Chicago: World Scientific Publishing Co. Pte. Ltd.
  • [37] Jang, J.-S.R. 1993. ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Systems, Man, and Cybernetics, vol. 23, no 3, pp 665-684.
  • [38] Jang J.S.R., Sun C.T., 1995. Neuro-Fuzzy Modeling and Control, Proc. IEEE, 83, 3, 378-406.
  • [39] Jang J.S.R., Sun C.T., Mizutani E., 1997. Neuro-Fuzzy and Soft Computing. Prentice Hall.
  • [40] A. Al-Hmouz, Jun Shen, R. Al-Hmouz, ve Jun Yan, “Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning”, IEEE Transactions on Learning Technologies, vol. 5, no 3, pp. 226-237, Tem. 2012.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Fırat Ekinci

Yayımlanma Tarihi 31 Temmuz 2019
Yayımlandığı Sayı Yıl 2019

Kaynak Göster

APA Ekinci, F. (2019). YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması. Duzce University Journal of Science and Technology, 7(3), 1029-1044. https://doi.org/10.29130/dubited.485822
AMA Ekinci F. YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması. DÜBİTED. Temmuz 2019;7(3):1029-1044. doi:10.29130/dubited.485822
Chicago Ekinci, Fırat. “YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması”. Duzce University Journal of Science and Technology 7, sy. 3 (Temmuz 2019): 1029-44. https://doi.org/10.29130/dubited.485822.
EndNote Ekinci F (01 Temmuz 2019) YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması. Duzce University Journal of Science and Technology 7 3 1029–1044.
IEEE F. Ekinci, “YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması”, DÜBİTED, c. 7, sy. 3, ss. 1029–1044, 2019, doi: 10.29130/dubited.485822.
ISNAD Ekinci, Fırat. “YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması”. Duzce University Journal of Science and Technology 7/3 (Temmuz 2019), 1029-1044. https://doi.org/10.29130/dubited.485822.
JAMA Ekinci F. YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması. DÜBİTED. 2019;7:1029–1044.
MLA Ekinci, Fırat. “YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması”. Duzce University Journal of Science and Technology, c. 7, sy. 3, 2019, ss. 1029-44, doi:10.29130/dubited.485822.
Vancouver Ekinci F. YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması. DÜBİTED. 2019;7(3):1029-44.

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