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DERİN ÖĞRENME TEKNİKLERİYLE TÜRKİYE GÜN ÖNCESİ PİYASASINDA ELEKTRİK FİYAT TAHMİNİ

Yıl 2022, Cilt: 9 Sayı: 2, 1433 - 1458, 29.07.2022
https://doi.org/10.30798/makuiibf.1097686

Öz

Gün Öncesi Piyasası, elektrik piyasası katılımcılarına gerçek zamandan bir gün öncesinde ticaret yapma imkânı sunan bir piyasadır. Gün Öncesi Piyasasında her saat için ayrı bir Piyasa Takas Fiyatı oluşturulmaktadır. Bu çalışmada, saatlik Piyasa Takas Fiyatının derin öğrenme teknikleri kullanılarak tahmin edilmesi amaçlanmıştır. Bu doğrultuda MLP, CNN, LSTM ve GRU modelleri ile 24 saatlik Piyasa Takas Fiyatı tahmin edilmiştir. Elde edilen sonuçlara göre, LSTM 8,15 MAPE değeri ile en iyi ortalama tahmin performansına sahip olmuştur. LSTM’i 8,44 MAPE değeri ile MLP, 8,72 MAPE değeri ile GRU ve 9,27 MAPE değeri ile CNN takip izlemiştir. Bu çalışmada kullanılan meteorolojik değişkenler için yenilebilir kaynaklarla üretim yapan santrallerin yoğun olduğu iller seçilmiştir. Yenilenebilir kaynaklarla elektrik üretimine olan eğilimin gelecekte daha da artması beklenmektedir. Bu bağlamda, piyasa katılımcıları için elektrik fiyat tahmininde bu kaynaklarla gerçekleşen üretimi etkileyebilecek faktörlerin göz önüne alınmasının önemli olduğu düşünülmektedir.

Destekleyen Kurum

AFYON KOCATEPE ÜNİVERSİTESİ BİLİMSEL ARAŞTIRMA PROJELERİ BİRİMİ

Proje Numarası

18.SOS.BİL.08

Kaynakça

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  • Althelaya, K. A., El-Alfy, E. M. and Mohammed, S. (2018). Stock Market Forecast Using Multivariate Analysis With Bidirectional and Stacked (LSTM, GRU). 21. Saudi Computer Society National Computer Conference (NCC), Riyad. https://doi.org/10.1109/NCG.2018.8593076
  • Amjady, N. and Hemmati, M. (2006). Energy Price Forecasting: Problems and Proposals For Such Predictions. IEEE Power Energy Magazine, March-April Issue, 20-29. https://doi.org/10.1109/MPAE.2006.1597990
  • Anbazhagan, S. and Kumarappan, N. (2014). Day-Ahead Deregulated Electricity Market Price Forecasting Using Neural Network Input Featured by DCT. Energy Conversion and Management, 78, 711-719. https://doi.org/10.1016/j.enconman.2013.11.031
  • Anochi, J. A. and Velho, H. F. C. (2016). Mesoscale Precipitation Climate Prediction for Brazilian South Region by Artificial Neural Networks. American Journal of Environmental Engineering, 6(4A), 94-102. https://doi.org/10.5923/s.ajee.201601.14
  • Bento, P. M. R., Pombo, J. A. N., Calado, M. R. A. and Mariano, S. J. P. S. (2018). A Bat Optimized Neural Network and Wavelet Transform Approach for Shortterm Price Forecasting. Applied Energy, 210, 88-97. https://doi.org/10.1016/j.apenergy.2017.10.058
  • Catalao, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F. and Ferreira, L. A. F. M. (2007). Short-Term Electricity Prices Forecasting in a Competitive Market: A Neural Network Approach. Electric Power Systems Research, 77, 1297-1304. https://doi.org/10.1016/j.epsr.2006.09.022
  • Cervone, A., Santini, E., Teodori, S. and Romito, D. Z. (2014). Electricity Price Forecast: A Comparison of Different Models to Evaluate the Single National Price in the Italian Energy Exchange Market. International Journal of Energy Economics and Policy, 4(4), 744-758.
  • Chaabane, N. (2014). A Hybrid ARFIMA and Neural Network Model for Electricity Price Prediction. Electrical Power and Energy Systems, 55, 187-194. https://doi.org/10.1016/j.ijepes.2013.09.004
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ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES

Yıl 2022, Cilt: 9 Sayı: 2, 1433 - 1458, 29.07.2022
https://doi.org/10.30798/makuiibf.1097686

Öz

Day-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. MLP followed the LSTM with 8.44 MAPE, GRU with 8.72 MAPE, and CNN with 9.27 MAPE. In the study, the provinces where the power plants producing with renewable resources are dense were selected for meteorological variables. It is expected that the trend towards electricity generation with renewable resources will increase in the future. In this context, it is thought important for market participants to consider the factors that may affect the production with these resources in the electricity price forecasting.

Proje Numarası

18.SOS.BİL.08

Kaynakça

  • Aineto, D., Sanchez, J. I., Zuniga, L. G. L., Onaindia, E. and Urcheguia, J. F. (2019). On The Influence of Renewable Energy Sources in Electricity Price Forecasting in The Iberian Market. Energies, 12(11), 1-20. https://doi.org/10.3390/en12112082
  • Althelaya, K. A., El-Alfy, E. M. and Mohammed, S. (2018). Stock Market Forecast Using Multivariate Analysis With Bidirectional and Stacked (LSTM, GRU). 21. Saudi Computer Society National Computer Conference (NCC), Riyad. https://doi.org/10.1109/NCG.2018.8593076
  • Amjady, N. and Hemmati, M. (2006). Energy Price Forecasting: Problems and Proposals For Such Predictions. IEEE Power Energy Magazine, March-April Issue, 20-29. https://doi.org/10.1109/MPAE.2006.1597990
  • Anbazhagan, S. and Kumarappan, N. (2014). Day-Ahead Deregulated Electricity Market Price Forecasting Using Neural Network Input Featured by DCT. Energy Conversion and Management, 78, 711-719. https://doi.org/10.1016/j.enconman.2013.11.031
  • Anochi, J. A. and Velho, H. F. C. (2016). Mesoscale Precipitation Climate Prediction for Brazilian South Region by Artificial Neural Networks. American Journal of Environmental Engineering, 6(4A), 94-102. https://doi.org/10.5923/s.ajee.201601.14
  • Bento, P. M. R., Pombo, J. A. N., Calado, M. R. A. and Mariano, S. J. P. S. (2018). A Bat Optimized Neural Network and Wavelet Transform Approach for Shortterm Price Forecasting. Applied Energy, 210, 88-97. https://doi.org/10.1016/j.apenergy.2017.10.058
  • Catalao, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F. and Ferreira, L. A. F. M. (2007). Short-Term Electricity Prices Forecasting in a Competitive Market: A Neural Network Approach. Electric Power Systems Research, 77, 1297-1304. https://doi.org/10.1016/j.epsr.2006.09.022
  • Cervone, A., Santini, E., Teodori, S. and Romito, D. Z. (2014). Electricity Price Forecast: A Comparison of Different Models to Evaluate the Single National Price in the Italian Energy Exchange Market. International Journal of Energy Economics and Policy, 4(4), 744-758.
  • Chaabane, N. (2014). A Hybrid ARFIMA and Neural Network Model for Electricity Price Prediction. Electrical Power and Energy Systems, 55, 187-194. https://doi.org/10.1016/j.ijepes.2013.09.004
  • Chang, Z., Zhang, Y. and Chen, W. (2018). Effective Adam-Optimized LSTM Neural Network for Electricity Price Forecasting. IEEE 9th International Conference on Software Engineering and Service Science, Beijing. https://doi.org/10.1109/ICSESS.2018.8663710
  • Cheng, H. Y., Kuo, P. H., Shen, Y. and Huang, C. J. (2020). Deep Convolutional Neural Network Model for Short-Term Electricity Price Forecasting. Retrieved from: https://arxiv.org/ftp/arxiv/papers/2003/2003.07202.pdf
  • Chung, J., Gülçehre, Ç., Cho, K. H. and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Retrieved from: https://arxiv.org/pdf/1412.3555.pdf
  • Contreras, J., Espinola, R., Nogales, F. J. and Conejo, A. J. (2003). ARIMA Models to Predict Next-Day Electricity Prices. IEEE Transactions Power Systems, 18(3), 1014-1020. https://doi.org/10.1109/TPWRS.2002.804943
  • Cuaresma, J. C., Hlouskova, J., Kossmeier, S. and Obersteiner, M. (2004). Forecasting Electricity Spotprices Using Linear Univariate Time-Series Models. Applied Energy, 77(1), 87-106. https://doi.org/10.1016/S0306-2619(03)00096-5
  • Devir, K. (2017). Türk Elektrik Piyasasının İşleyişi. Bursa, Dora Publications.
  • EPİAŞ (2016a). Elektrik Piyasası Özet Bilgiler Raporu 2016. Retrieved from: https://www.epias.com.tr/wp-content/uploads/2017/03/EPIAS_2016_Yili_ Elektrik_Piyasasi_Ozet_Bilgiler_Raporu.pdf
  • EPİAŞ (2016b). Gün Öncesi Elektrik Piyasası Piyasa Takas Fiyatı Belirleme Yöntemi. İstanbul, EPİAŞ Publications.
  • EPİAŞ (2017). Elektrik Piyasası Özet Bilgiler Raporu 2017. Retrieved from: https://www.epias.com.tr/wp-content/uploads/2018/03/EPIAS_2017_Yillik_ Bulten_V2.pdf
  • EPİAŞ (2018). Elektrik Piyasası Özet Bilgiler Raporu 2018. Retrieved from: https://www.epias.com.tr/wp-content/uploads/2019/03/Epias_2018_Yillik_ Bulten.pdf
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Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makaleleri
Yazarlar

Arif Arifoğlu 0000-0003-3361-6760

Tuğrul Kandemir 0000-0002-3544-7422

Proje Numarası 18.SOS.BİL.08
Yayımlanma Tarihi 29 Temmuz 2022
Gönderilme Tarihi 2 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 9 Sayı: 2

Kaynak Göster

APA Arifoğlu, A., & Kandemir, T. (2022). ELECTRICITY PRICE FORECASTING IN TURKISH DAY-AHEAD MARKET VIA DEEP LEARNING TECHNIQUES. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 9(2), 1433-1458. https://doi.org/10.30798/makuiibf.1097686