Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2021, Cilt: 9 Sayı: 4, 398 - 403, 30.10.2021
https://doi.org/10.17694/bajece.929564

Öz

Kaynakça

  • Y. Biçen, “Türkiye elektrik enerjisi piyasası gelişim süreci: Gün öncesi ve dengeleme güç piyasası özellikleri.” Karaelmas Sci. Eng. J., vol. 6. 2, 2016, pp. 432–438.
  • Electricity market Law No: 28603, Official Gazette of the Republic of Turkey, 6446, 5 (53). March 2013.
  • 2017 EPİAŞ Activity report of the board. 2 March 2018; Available from: https://www.epias.com.tr/en/corporate/annual-reports/.
  • P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, and A. K. Srivastava, “A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method.” IEEE Trans. Power Syst., vol. 22. 4, 2007, pp. 2058–2065.
  • I. Y. Zolotova and V. V Dvorkin, “Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks.” Stud. Russ. Econ. Dev., vol. 28. 6, 2017, pp. 608–615.
  • K. K. Nargale and S. B. Patil, “Day ahead price forecasting in deregulated electricity market using Artificial Neural Network.” in 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), India, 2016.
  • F. Saâdaoui, “A seasonal feedforward neural network to forecast electricity prices.” Neural Comput. Appl., vol. 28. 4, 2017, pp. 835–847.
  • D. Keles, J. Scelle, F. Paraschiv, and W. Fichtner, “Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks.” Appl. Energy, vol. 162, 2016, pp. 218–230.
  • F. Şenocak and H. Kahveci, “Periodic price avarages forecasting of MCP in day-ahead market.” in National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), Turkey, 2016.
  • A. Dalgın, “Yapay Sinir Ağları Kullanılarak Türkiye Gün Öncesi Piyasası Elektrik Fiyat Tahmini.” ITU Energy Institute, Turkey, 2017.
  • I. N. Da Silva, D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. dos Reis Alves, “Artificial neural network architectures and training processes,” in Artificial neural networks, Springer, 2017, pp. 21–28.
  • O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey.” Heliyon, vol. 4. 11, 2018, pp. e00938.
  • R. Hecht-Nielsen, “Theory of the backpropagation neural network,” in Neural networks for perception, Elsevier, 1992, pp. 65–93.

Forecast for Market Clearing Price with Artificial Neural Networks in Day Ahead Market

Yıl 2021, Cilt: 9 Sayı: 4, 398 - 403, 30.10.2021
https://doi.org/10.17694/bajece.929564

Öz

In this study, the Market Clearing Price (MCP) is forecasted with Artificial Neural Networks and the modeling success is examined for different preprocessing strategies. The purpose of the study is to obtain the optimum model with a significant estimation success and to provide the best price prediction. The hour-based electricity generation data of diverse production items are assigned as inputs and the resulting MCP is modeled. The raw data are first cleaned from outliers, then subjected to different normalization processes and 70 different ANNs are trained. Additionally, networks are trained with data classified in seasons and the effect of seasonal patterns on model success is observed. Finally, networks showing the optimum performance are selected. It is noted that the type of the normalization strategy and the hidden layer size are the key factors to make a decent estimation. Then, in order to test the networks with extreme cases, data for the special days (official holidays) are applied to these networks as input. The success of the networks is evaluated by comparing the MCP predictions with the actual values. It is revealed to make a prediction for official holidays, a model which is special to this period of year is required.

Kaynakça

  • Y. Biçen, “Türkiye elektrik enerjisi piyasası gelişim süreci: Gün öncesi ve dengeleme güç piyasası özellikleri.” Karaelmas Sci. Eng. J., vol. 6. 2, 2016, pp. 432–438.
  • Electricity market Law No: 28603, Official Gazette of the Republic of Turkey, 6446, 5 (53). March 2013.
  • 2017 EPİAŞ Activity report of the board. 2 March 2018; Available from: https://www.epias.com.tr/en/corporate/annual-reports/.
  • P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, and A. K. Srivastava, “A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method.” IEEE Trans. Power Syst., vol. 22. 4, 2007, pp. 2058–2065.
  • I. Y. Zolotova and V. V Dvorkin, “Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks.” Stud. Russ. Econ. Dev., vol. 28. 6, 2017, pp. 608–615.
  • K. K. Nargale and S. B. Patil, “Day ahead price forecasting in deregulated electricity market using Artificial Neural Network.” in 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), India, 2016.
  • F. Saâdaoui, “A seasonal feedforward neural network to forecast electricity prices.” Neural Comput. Appl., vol. 28. 4, 2017, pp. 835–847.
  • D. Keles, J. Scelle, F. Paraschiv, and W. Fichtner, “Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks.” Appl. Energy, vol. 162, 2016, pp. 218–230.
  • F. Şenocak and H. Kahveci, “Periodic price avarages forecasting of MCP in day-ahead market.” in National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), Turkey, 2016.
  • A. Dalgın, “Yapay Sinir Ağları Kullanılarak Türkiye Gün Öncesi Piyasası Elektrik Fiyat Tahmini.” ITU Energy Institute, Turkey, 2017.
  • I. N. Da Silva, D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. dos Reis Alves, “Artificial neural network architectures and training processes,” in Artificial neural networks, Springer, 2017, pp. 21–28.
  • O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey.” Heliyon, vol. 4. 11, 2018, pp. e00938.
  • R. Hecht-Nielsen, “Theory of the backpropagation neural network,” in Neural networks for perception, Elsevier, 1992, pp. 65–93.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Oğuz Tonyalı 0000-0002-2604-519X

Duygu Bayram 0000-0001-8184-8510

Yayımlanma Tarihi 30 Ekim 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 4

Kaynak Göster

APA Tonyalı, O., & Bayram, D. (2021). Forecast for Market Clearing Price with Artificial Neural Networks in Day Ahead Market. Balkan Journal of Electrical and Computer Engineering, 9(4), 398-403. https://doi.org/10.17694/bajece.929564

All articles published by BAJECE are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited.Creative Commons Lisansı