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Mid-Term Electrical Energy Consumption Forecasting Using Artificial Neural Networks: A case study for İskenderun

Yıl 2021, Sayı: 28, 489 - 492, 30.11.2021
https://doi.org/10.31590/ejosat.1007589

Öz

The use of smart grid systems in electrical power systems, the operation and maintenance of power systems, and the increase in the use of renewable energy sources have made the estimation of electrical energy consumption very important. Linear and non-stationary features affecting energy consumption make energy consumption estimation difficult. Mid-term electrical energy consumption forecasting include forecastings from 2 weeks to 3 years and the efficient operation of power systems is very important in terms of determining the energy needs correctly and taking the necessary precautions and determining the needs of the companies working in this field. In this study, mid-term electrical energy consumption forecasting was made for İskenderun using artificial neural networks. The results showed that the developed artificial neural network can be used for mid-term electrical energy consumption forecasting for Iskenderun and the forecasting performance of this model is high. The results showed that this study can provide the needs of companies and big government facilities that invest in renewable energy technologies and operate in this field.

Kaynakça

  • J. L. Harris, L.M. Liu, “Dynamic structural analysis and forecasting of residential electricity consumption,” Int. J. Forecast, vol. 9, pp.437-455, 2007.
  • T. Hong, S. Fan, “Probabilistic electric load forecasting: A tutorial review,” Int. J. Forecast, vol.32, pp.914-938, 2016.
  • A. Rahman, V. Srikumar, A.D. Smith. “Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks,” Appl. Energy, vol.212, pp. 372-385, 2018.
  • Z. Yumurtaci, E. Asmaz, “Electric energy demand of Turkey for the year 2050,” Energy Source, vol.26, pp.1157–1164, 2004.
  • Z. Shao, F. Gao, Q. Zhang, S. Yang, “Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: a novel approach to the case study of mid-long term electricity consumption forecasting in China,” Appl. Energy, vol.156, pp. 502-518, 2015.
  • Z. Hu, Y. Bao, R. Chiong, T. Xiong, “Mid-term interval load forecasting using multioutput support vector regression with a memetic algorithm for feature selection,” Energy, vol.84, pp. 419-431, 2015.
  • C. Cecati, J. Kolbusz, P. Rozycki, P. Siano, and B. M. Wilamowski. “A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies,” IEEE Trans. Ind. Electron., vol. 62, pp. 6519-6529, 2015.
  • S. M. Barakati, S. M. R Rafiei, A. A. Gharaveisi, “Short-Term Load Forecasting Using Mixed Lazy Learning Method,” Turkish Journal of Electrical Engineering and Computer Science, vol. 23, pp 201-211, 2015.
  • K. Amber, R. Ahmad, M. Aslam, A. Kousar, M. Usman, M. Khan, “Intelligent techniques for forecasting electricity consumption of buildings,” Energy, vol.157, pp. 886-893, 2018.
  • C. Deb, F. Zhang, J. Yang, S.E. Lee, K.W. Shah, “A review on time series forecasting techniques for building energy consumption,” Renewable and Sustainable Energy Reviews, vol.74, pp. 902-924, 2017.
  • S. Barak, S.S. Sadegh, “Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm,” International Journal of Electrical Power & Energy Systems, vol. 82, pp. 92-104, 2016.
  • A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, F, H. Abdullah, R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renew. Sustain. Energy Rev, vol.33, pp.102-109, 2014.
  • S.H.A. Kaboli, A. Fallahpour, J. Selvaraj, N. Rahim, “Long-Term Electrical Energy Consumption Formulating and Forecasting via Optimized Gene Expression Programming”, Energy, vol. 126, pp 144-164, 2017.
  • J. Runge, R. Zmeureanu, “Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review,” Energies, vol.12, 2019.
  • F. M. Ham, I. Kostanic, “Principles of Neurocomputing for Science and Engineering,” New York, Mc-Graw Hill, 2001.
  • Y. Kung, Digital Neural Networks, PTR Prentice Hall, 1998.
  • B. Bose, “Neural Network Principles and Applications,” Prentice Hall PTR, United States of America, 2002.

Yapay Sinir Ağları Kullanılarak Orta Dönem Elektrik Enerjisi Tüketim Tahmini: İskenderun Örneği

Yıl 2021, Sayı: 28, 489 - 492, 30.11.2021
https://doi.org/10.31590/ejosat.1007589

Öz

Akıllı şebeke sistemlerinin son zamanlarda elektrik güç sistemlerinde kullanılması, güç sistemlerinin çalışması ve sürdürebilmesi, yenilenebilir enerji kaynaklarının kullanımının artması elektrik enerjisi tüketimi tahminini oldukça önemli bir hale getirmiştir. Enerji tüketimini etkileyen doğrusal ve durağan olmayan özellikler enerji tüketim tahminini zorlaştırmaktadır. Orta dönem elektrik enerjisi tüketimi tahminleri 2 haftadan 3 yıla kadar olan tahminleri içermektedir ve güç sistemlerinin verimli çalışması, enerji ihtiyaçlarının doğru belirlenerek gerekli önlemlerin alınması ve bu alanda çalışan şirketlerin ihtiyaçlarının belirlenmesi açısından oldukça önemlidir. Bu çalışmada yapay sinir ağları kullanılarak İskenderun için orta dönem elektrik enerjisi tüketim tahmini yapılmıştır. Elde edilen sonuçlar geliştirilen yapar sinir ağının İskenderun orta dönem elektrik enerjisi tüketim tahmini için kullanılabileceğini ve bu modelin tahmin performansının yüksek olduğunu göstermiştir. Sonuçlar bu çalışmanın yenilenebilir enerji teknolojileriyle ilgili yatırım yapan ve bu alanda faaliyet gösteren şirketler ve büyük devlet tesislerinin bu alandaki ihtiyaçlarını karşılayabileceğini göstermektedir.

Kaynakça

  • J. L. Harris, L.M. Liu, “Dynamic structural analysis and forecasting of residential electricity consumption,” Int. J. Forecast, vol. 9, pp.437-455, 2007.
  • T. Hong, S. Fan, “Probabilistic electric load forecasting: A tutorial review,” Int. J. Forecast, vol.32, pp.914-938, 2016.
  • A. Rahman, V. Srikumar, A.D. Smith. “Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks,” Appl. Energy, vol.212, pp. 372-385, 2018.
  • Z. Yumurtaci, E. Asmaz, “Electric energy demand of Turkey for the year 2050,” Energy Source, vol.26, pp.1157–1164, 2004.
  • Z. Shao, F. Gao, Q. Zhang, S. Yang, “Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: a novel approach to the case study of mid-long term electricity consumption forecasting in China,” Appl. Energy, vol.156, pp. 502-518, 2015.
  • Z. Hu, Y. Bao, R. Chiong, T. Xiong, “Mid-term interval load forecasting using multioutput support vector regression with a memetic algorithm for feature selection,” Energy, vol.84, pp. 419-431, 2015.
  • C. Cecati, J. Kolbusz, P. Rozycki, P. Siano, and B. M. Wilamowski. “A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies,” IEEE Trans. Ind. Electron., vol. 62, pp. 6519-6529, 2015.
  • S. M. Barakati, S. M. R Rafiei, A. A. Gharaveisi, “Short-Term Load Forecasting Using Mixed Lazy Learning Method,” Turkish Journal of Electrical Engineering and Computer Science, vol. 23, pp 201-211, 2015.
  • K. Amber, R. Ahmad, M. Aslam, A. Kousar, M. Usman, M. Khan, “Intelligent techniques for forecasting electricity consumption of buildings,” Energy, vol.157, pp. 886-893, 2018.
  • C. Deb, F. Zhang, J. Yang, S.E. Lee, K.W. Shah, “A review on time series forecasting techniques for building energy consumption,” Renewable and Sustainable Energy Reviews, vol.74, pp. 902-924, 2017.
  • S. Barak, S.S. Sadegh, “Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm,” International Journal of Electrical Power & Energy Systems, vol. 82, pp. 92-104, 2016.
  • A. Ahmad, M. Hassan, M. Abdullah, H. Rahman, F. Hussin, F, H. Abdullah, R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renew. Sustain. Energy Rev, vol.33, pp.102-109, 2014.
  • S.H.A. Kaboli, A. Fallahpour, J. Selvaraj, N. Rahim, “Long-Term Electrical Energy Consumption Formulating and Forecasting via Optimized Gene Expression Programming”, Energy, vol. 126, pp 144-164, 2017.
  • J. Runge, R. Zmeureanu, “Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review,” Energies, vol.12, 2019.
  • F. M. Ham, I. Kostanic, “Principles of Neurocomputing for Science and Engineering,” New York, Mc-Graw Hill, 2001.
  • Y. Kung, Digital Neural Networks, PTR Prentice Hall, 1998.
  • B. Bose, “Neural Network Principles and Applications,” Prentice Hall PTR, United States of America, 2002.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Merve Erkınay Özdemir 0000-0001-8864-385X

Yayımlanma Tarihi 30 Kasım 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 28

Kaynak Göster

APA Erkınay Özdemir, M. (2021). Yapay Sinir Ağları Kullanılarak Orta Dönem Elektrik Enerjisi Tüketim Tahmini: İskenderun Örneği. Avrupa Bilim Ve Teknoloji Dergisi(28), 489-492. https://doi.org/10.31590/ejosat.1007589