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Türkiye Sanayi Elektrik Enerjisi Tüketiminin 2017-2023 dönemi için Yapay Sinir Ağları ile Tahmini

Yıl 2019, Cilt: 3 Sayı: 2, 206 - 228, 30.09.2019

İhsan Pençe Adnan Kalkan Melike Şişeci Çeşmeli

https://doi.org/10.31200/makuubd.538878
Cited By: 6

Öz

Ülkelerin
gelişmesinde sanayinin büyük bir rolü olup geçmişten günümüze kadar sanayi
faaliyetleri hız kesmeden ilerlemiştir. Bu gelişime ayak uyduran ülkeler ucuz
hammaddeleri işleyip yüksek ücretlere satarak hazinelerini genişletmişlerdir.
Endüstri 4.0 devriminin şafağında bu gelişimden geri kalınmaması gerekmekte
olup gerek sanayi gerekse teknoloji birlikte geliştirilmelidir. Sanayileşmedeki
en büyük ihtiyaçlardan biri elektrik enerjisi olup Türkiye’de elektrik enerjisi
tüketiminin sanayi için oranları yıllara göre %40 ile %60 arasında
değişmektedir. Bu oranlar düşünüldüğünde elektrik tüketiminin büyük bir payı
sanayiye ait olup ileriye yönelik planlamaların yapılmasına kesinlikle ihtiyaç
duyulmaktadır. Türkiye’nin Endüstri 4.0 ile birlikte gelecek planlarında
elektrik enerjisi sıkıntısına düşmemesi için ileriye yönelik tahminleme ve buna
uygun yeni tesislerin kurulumlarının planlanması gerekmektedir. Bu çalışmada,
Türkiye’de 1970-2016 yıllarına ait sanayi için elektrik tüketimleri yapay sinir
ağları ile modellenmiş olup elde edilen model daha sonra 2017-2023 yıllarındaki
tüketimi tahmin etmek için kullanılmıştır. Yapay sinir ağı birisi-dışarıda
çapraz doğrulama yöntemi ile test edilmiş olup elde edilen sonuçlara göre;
ortalama karesel hataların karekökü değeri 8.99, ortalama mutlak yüzde hata
%31.6 ve belirleme katsayısı ise 0.94 olarak elde edilmiş olup bu sonuçlar
modelin iyi kurulduğunu ortaya koymaktadır. Ayrıca 2023 yılına kadar olan
tahmin değerleri de Türkiye Elektrik İletim A.Ş. Genel Müdürlüğü’nün kendi
tahminleri ile paralellik göstermektedir.

Anahtar Kelimeler

Elektrik Tüketimi Sanayi Yapay Sinir Ağları Tahminleme

Kaynakça

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  • Akay, D. & Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.
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  • Balcı, H., Esener, İ. I. & Kurban, M. (2012). Regresyon analizi kullanılarak kısa dönem yük tahmini. Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyumu, 796-801.
  • Barak, S. & Sadegh, S. S. (2016). Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. International Journal of Electrical Power & Energy Systems, 82, 92-104.
  • Başoğlu, B. & Bulut, M. (2017). Kısa dönem elektrik talep tahminleri için yapay sinir ağları ve uzman sistemler tabanlı hibrid tahmin sistemi geliştirilmesi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32(2).
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  • Behrang, M. A., Assareh, E., Assari, M. R. & Ghanbarzadeh, A. (2011). Assessment of electricity demand in Iran's industrial sector using different intelligent optimization techniques. Applied Artificial Intelligence, 25(4), 292-304.
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  • Bianco, V., Manca, O. & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421.
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  • Bi̇ri̇ci̇k, G., Bozkurt, Ö. Ö. & Tayşi̇, Z. C. (2015). Analysis of features used in short-term electricity price forecasting for deregulated markets. IEEE 23th Signal Processing and Communications Applications Conference (SIU), 600-603.
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  • Demirel, Ö., Kakilli, A. & Tektaş, M. (2010). Anfis ve arma modelleri ile elektrik enerjisi yük tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 25(3).
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Estimation of Turkey Industrial Electricity Consumption with Artificial Neural Networks for the 2017-2023 Period

Yıl 2019, Cilt: 3 Sayı: 2, 206 - 228, 30.09.2019

İhsan Pençe Adnan Kalkan Melike Şişeci Çeşmeli

https://doi.org/10.31200/makuubd.538878
Cited By: 6

Öz

In
the development of the countries, the industry played a big role and the
industrial activities from the past to the present day progressed without
slowing down. These developments have expanded their treasury by selling cheap
raw materials to high wages after they have grown up. At the dawn of the
Industry 4.0 revolution, this development should not be left behind, and both
industry and technology should be developed together. For industrialization,
one of the biggest needs is electricity energy and the ratio of consumption of
electricity energy in proportion to industry in Turkey ranges from 40% to 60%
according to years. Considering these rates, a large share of electricity
consumption belongs to the industry and there is absolutely a need for future planning.
Turkey's future plans with Industry 4.0, forecasting electricity for onward
fall into distress, and it is necessary to install the proper planning of new
facilities. In this study, the electricity consumption of the 1970-2016 years
for the industry in Turkey are modeled with artificial neural network, then
obtained successful model is used to estimate consumption in the years
2017-2023. The artificial neural network has been tested by leave-one-out cross
validation method and according to the results; the root mean square error is
8.99, the mean absolute percentage error is 31.6%, and the coefficient of
determination is 0.94, which indicates that the model is well established. In
addition, forecast values up to 2023 are in line with General Directorate of
Türkiye Elektrik İletim A.Ş.'s own estimates. 

Anahtar Kelimeler

Electricity Consumption Industry Artificial Neural Networks Estimation

Kaynakça

  • Adom, P. K. & Bekoe, W. (2012). Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: a comparison of ARDL and PAM. Energy, 44(1), 367-380.
  • Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H. & Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102-109.
  • Akay, D. & Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.
  • Akyılmaz, O. & Ayan, T. (2006). Esnek hesaplama yöntemlerinin jeodezide uygulamaları. İTÜ Dergisi, 5(1), 261-268.
  • Amber, K. P., Aslam, M. W. & Hussain, S. K. (2015). Electricity consumption forecasting models for administration buildings of the UK higher education sector. Energy and Buildings, 90, 127-136.
  • Ambroise, C. & McLachlan, G.J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Nat. Acad. Sci. USA, 99(10), 6562-6566.
  • Arisoy, I. & Ozturk, I. (2014). Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach. Energy, 66, 959-964.
  • Arslan, A. & İnce, R. (1996). The Neural network approximation to the size effect in fracture of cemetitious materials. Engineering Fracture Mechanics, 54(2), 249-261.
  • Aydoğdu, G. & Yildiz, O. (2017). Forecasting the annual electricity consumption of Turkey using a hybrid model. IEEE 25th Signal Processing and Communications Applications Conference (SIU), 1-4.
  • Babich, L., Svalov, D., Smirnov, A. & Babich, M. (2019). Industrial power consumption forecasting methods comparison. In 2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), IEEE, 307-309.
  • Balcı, H., Esener, İ. I. & Kurban, M. (2012). Regresyon analizi kullanılarak kısa dönem yük tahmini. Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyumu, 796-801.
  • Barak, S. & Sadegh, S. S. (2016). Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. International Journal of Electrical Power & Energy Systems, 82, 92-104.
  • Başoğlu, B. & Bulut, M. (2017). Kısa dönem elektrik talep tahminleri için yapay sinir ağları ve uzman sistemler tabanlı hibrid tahmin sistemi geliştirilmesi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32(2).
  • Bayramoğlu, T., Pabuçcu, H. & Boz, F. Ç. (2017). Türkiye için anfis modeli ile birincil enerji talep tahmini. Ege Akademik Bakis, 17(3), 431-445.
  • Behrang, M. A., Assareh, E., Assari, M. R. & Ghanbarzadeh, A. (2011). Assessment of electricity demand in Iran's industrial sector using different intelligent optimization techniques. Applied Artificial Intelligence, 25(4), 292-304.
  • Bianco, V., Manca, O. & Nardini, S. (2013). Linear regression models to forecast electricity consumption in Italy. Energy Sources, Part B: Economics, Planning, and Policy, 8(1), 86-93.
  • Bianco, V., Manca, O. & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421.
  • Bilgili, M., Sahin, B., Yasar, A. & Simsek, E. (2012). Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews, 16(1), 404-414.
  • Bi̇ri̇ci̇k, G., Bozkurt, Ö. Ö. & Tayşi̇, Z. C. (2015). Analysis of features used in short-term electricity price forecasting for deregulated markets. IEEE 23th Signal Processing and Communications Applications Conference (SIU), 600-603.
  • Boltürk, E. (2013). Elektrik talebi tahmininde kullanılan yöntemlerin karşılaştırılması (Yüksek lisans tezi). İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Burden, F. & Winkler, D. (2008). Bayesian regularization of neural networks. In Artificial neural networks, Humana Press, 23-42.
  • Cabral, J. D. A. Legey, L. F. L. & Freitas Cabral, M. V. D. (2017). Electricity consumption forecasting in Brazil: A spatial econometrics approach. Energy, 126, 124-131.
  • Cao, G. & Wu, L. (2016). Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting. Energy, 115, 734-745.
  • Chae, Y. T., Horesh, R., Hwang, Y. & Lee, Y. M. (2016). Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy and Buildings, 111, 184-194.
  • Chou, J. S., Hsu, S. C., Ngo, N. T., Lin, C. W. & Tsui, C. C. (2019). Hybrid machine learning system to forecast electricity consumption of smart grid-based air conditioners. IEEE Systems Journal.
  • Çalık, A. E. & Şirin, H. (2017). Türkiye’deki elektrik enerji ihtiyacının matematiksel bir modellemesi. Sakarya University Journal of Science, 21(6), 1475-1482.
  • Demirel, Ö., Kakilli, A. & Tektaş, M. (2010). Anfis ve arma modelleri ile elektrik enerjisi yük tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 25(3).
  • Dilaver, Z. & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
  • Doan, C. D. & Liong, S. Y. (2004). Generalization for multilayer neural network bayesian regularization or early stopping. In Proceedings of Asia Pacific Association of Hydrology and Water Resources 2nd Conference.
  • Dong, B., Li, Z., Rahman, S. M. & Vega, R. (2016). A hybrid model approach for forecasting future residential electricity consumption. Energy and Buildings, 117, 341-351.
  • Eke, İ. (2011). Diferansiyel evrim algoritması destekli yapay sinir ağı ile orta dönem yük tahmini. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 3(1), 28-32.
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  • Kasule, A. & Ayan, K. (2019). Forecasting uganda’s net electricity consumption using a hybrid pso-abc algorithm. Arabian Journal for Science and Engineering, 44(4), 3021-3031.
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  • Kayabasi, A. (2015). Kompakt mikroşerit antenlerin rezonans frekansının yapay sinir ağları ve bulanık mantık sistemine dayalı uyarlanır ağ kullanarak hesaplanması (Doktora tezi). Mersin Üniversitesi, Fen Bilimleri Enstitüsü, Mersin.
  • Kaynar, O., Yüksek, A. G. & Demirkoparan, F. (2016). Forecasting of Turkey's electricity consumption using support vector regression trained with genetic algorithm. Istanbul Üniversitesi Iktisat Fakültesi Mecmuasi, 66(2), 45-60.
  • Kaytez, F., Taplamacioglu, M. C., Cam, E. & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431-438.
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Toplam 80 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İhsan Pençe 0000-0003-0734-3869

Adnan Kalkan 0000-0002-2270-4100

Melike Şişeci Çeşmeli 0000-0001-9541-2590

Yayımlanma Tarihi 30 Eylül 2019
Kabul Tarihi 5 Temmuz 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 3 Sayı: 2

Kaynak Göster

APA Pençe, İ., Kalkan, A., & Şişeci Çeşmeli, M. (2019). Türkiye Sanayi Elektrik Enerjisi Tüketiminin 2017-2023 dönemi için Yapay Sinir Ağları ile Tahmini. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 3(2), 206-228. https://doi.org/10.31200/makuubd.538878

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