Türkiye Enerji Piyasasında Piyasa Takas Fiyatı Tahmini: Makine Öğrenimi Yöntemlerinin Karşılaştırılması

Arenas-Garcia, J., Petersen, K. B., Camps-Valls, G., & Hansen, L. K. (2013). Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods. IEEE Signal Processing Magazine, 30(4), 16–29. https://doi.org/10.1109/MSP.2013.2250591

Bento, P. M. R., Pombo, J. A. N., Calado, M. R. A., & Mariano, S. J. P. S. (2018). A bat optimized neural network and wavelet transform approach for short-term price forecasting. Applied Energy, 210, 88–97. https://doi.org/10.1016/J.APENERGY.2017.10.058

Bertsimas, D., & King, A. (2016). OR Forum—An Algorithmic Approach to Linear Regression. Operations Research, 64(1), 2–16. https://doi.org/10.1287/opre.2015.1436

Bilgili, M., Ilhan, A., & Ünal, Ş. (2022). Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches. Neural Computing and Applications, 34(18), 15633–15648. https://doi.org/10.1007/s00521-022-07275-5

Cerjan, M., Petričić, A., & Delimar, M. (2019). HIRA Model for Short-Term Electricity Price Forecasting. Energies 2019, Vol. 12, Page 568, 12(3), 568. https://doi.org/10.3390/en12030568

Demircioğlu, M., & Eşİyok, S. (2022). Energy consumption forecast of Turkey using artificial neural networks from a sustainability perspective. International Journal of Sustainable Energy, 41(8), 1127–1141. https://doi.org/10.1080/14786451.2022.2026357

EPİAŞ. (2024). Enerji Piyasaları İşletme A.Ş. Erişim adresi: https://www.epias.com.tr

Gao, G., Lo, K., & Fan, F. (2010). Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market. 9, 120–126. https://doi.org/10.4236/epe.2017.94B015

Hamzaçebi, C., Es, H. A., & Çakmak, R. (2019). Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network. Neural Computing and Applications, 31(7), 2217–2231. https://doi.org/10.1007/s00521-017-3183-5

Kalfa, V. R., Arslan, B., & Ertuğrul, İ. (2021). Determining the Factors Affecting the Market Clearing Price by Using Multiple Linear Regression Method. Alphanumeric Journal, 9(1), 35–48. https://doi.org/10.17093/alphanumeric.882847

Karim, R., Alam, M. K., & Hossain, M. R. (2021). Stock market analysis using linear regression and decision tree regression. 2021 1st International Conference on Emerging Smart Technologies and Applications, ESmarTA 2021. https://doi.org/10.1109/esmarta52612.2021.9515762

Kemal, M., Y., Kucukcolak, N. I., & Kucukcolak, R. A. (2018). Market efficiency and risk premium in the Turkish wholesale electricity market. International Journal of Energy Economics and Policy, 8(5), 76–88.

Keskin, A., Ersin, I., & Atalan, A. (2024). Prıce Estımatıon Of Selected Graıns Products Based On Machıne Learnıng For Agrıcultural Economıc Development In Türkiye. The Journal of Animal and Plant Sciences, (5), 1290–1302. https://doi.org/10.36899/japs.2024.5.0811

Keskin, A., & Kara, B. (2021). The Relationship Between Electricity Generation, Electricity Consumption, and Economic Growth in Turkey 1975-2019. https://doi.org/10.4018/978-1-7998-8335-7.ch012

Keskin, A. (2025). Predicting and Reducing Patient Waiting Times in Dental Clinics Using Machine Learning: A Case Study from Türkiye. Black Sea Journal of Engineering and Science, 8(1), 243–248. https://doi.org/10.34248/bsengıneerıng.1574470

Khajeh, M. G., Maleki, A., Rosen, M. A., & Ahmadi, M. H. (2018). Electricity price forecasting using neural networks with an improved iterative training algorithm. International Journal of Ambient Energy, 39(2), 147–158. https://doi.org/10.1080/01430750.2016.1269674;wgroup:strıng:publıcatıon

Kölmek, M. A., & Navruz, I. (2015). Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks. Turkish Journal of Electrical Engineering and Computer Sciences, 23(3), 841–852. https://doi.org/10.3906/elk-1212-136

Kruyt, B., van Vuuren, D. P., de Vries, H. J. M., & Groenenberg, H. (2009). Indicators for energy security. Energy Policy, 37(6), 2166–2181. https://doi.org/10.1016/j.enpol.2009.02.006

Kuo, P. H., & Huang, C. J. (2018). An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks. Sustainability 2018, Vol. 10, Page 1280, 10(4), 1280. https://doi.org/10.3390/su10041280

Lago, J., De Ridder, F., & De Schutter, B. (2018). Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Applied Energy, 221, 386–405. https://doi.org/10.1016/j.apenergy.2018.02.069

Li, K., Cursio, J. D., Jiang, M., & Liang, X. (2019). The significance of calendar effects in the electricity market. Applied Energy, 235, 487–494. https://doi.org/10.1016/j.apenergy.2018.10.124

Marrel, A., & Iooss, B. (2024). Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation. Reliability Engineering & System Safety, 247, 110094. https://doi.org/10.1016/j.ress.2024.110094

Mohammadi, S., Hesamzadeh, M. R., Vafamehr, A., & Ferdowsi, F. (2020). A Review of Machine Learning Applications in Electricity Market Studies. Proceedings of 2020 3rd International Colloquium on Intelligent Grid Metrology, smagrımet 2020, 15–22. https://doi.org/10.23919/smagrımet48809.2020.9264022

Naskath, J., Sivakamasundari, G., & Begum, A. A. S. (2023). A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN. Wireless Personal Communications, 128(4), 2913–2936. https://doi.org/10.1007/S11277-022-10079-4/fıgures/7

Neupane, B., LeeWoon, W., & Aung, Z. (2017). Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting. Energies 2017, Vol. 10, Page 77, 10(1), 77. https://doi.org/10.3390/en10010077

Polson, M., & Sokolov, V. (2020). Deep learning for energy markets. Applied Stochastic Models in Business and Industry, 36(1), 195–209. https://doi.org/10.1002/asm.2518

Purlu, M., Turkay, B. E., Andic, C., Aydin, E., Canol, B., & Kucukaslan, B. (2022). Market-Clearing Price Forecasting Using Keras in Turkish Day-Ahead Electricity Market. Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022, 517–522. https://doi.org/10.1109/gpecom55404.2022.9815603

Rafiei, M., Niknam, T., & Khooban, M. H. (2017). Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network. IEEE Transactions on Industrial Informatics, 13(1), 71–79. https://doi.org/10.1109/tıı.2016.2585378

Rao, A., Tedeschi, M., Mohammed, K. S., & Shahzad, U. (2024). Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach. Computational Economics, 64(6), 3295–3315. https://doi.org/10.1007/S10614-024-10550-3/tables/4

Singh, N., Mohanty, S. R., & Dev Shukla, R. (2017). Short term electricity price forecast based on environmentally adapted generalized neuron. Energy, 125, 127–139. https://doi.org/10.1016/j.energy.2017.02.094

Song, Y.-Y., & Lu, Y. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi.org/10.11919/j.issn.1002-0829.215044

The MathWorks, Inc. (2024). MATLAB and Statistics Toolbox Release [2024a], Regression Learner. Massachusetts: The MathWorks, Inc.

Tschora, L., Pierre, E., Plantevit, M., & Robardet, C. (2022). Electricity price forecasting on the day-ahead market using machine learning. Applied Energy, 313, 118752. https://doi.org/10.1016/J.apenergy.2022.118752

Ugurlu, U., Oksuz, I., & Tas, O. (2018). Electricity Price Forecasting Using Recurrent Neural Networks. Energies 2018, Vol. 11, Page 1255, 11(5), 1255. https://doi.org/10.3390/en11051255

Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030–1081. https://doi.org/10.1016/j.ıjforecast.2014.08.008

Winzer, C. (2012). Conceptualizing energy security. Energy Policy, 46, 36–48. https://doi.org/10.1016/j.enpol.2012.02.067

Yang, W., Sun, S., Hao, Y., & Wang, S. (2022). A novel machine learning-based electricity price forecasting model based on optimal model selection strategy. Energy, 238, 121989. https://doi.org/10.1016/J.ENERGY.2021.121989

Yorulmuş, H., Uğurlu, U., & Taş, O. (2018). A long short term memory application on the Turkish intraday electricity price forecasting. PressAcademia Procedia, 7(1), 126–130. https://doi.org/10.17261/pressacademia.2018.867

Yunusova, P., & Kiran, M. S. (2022). Tree-Seed Programming for Modelling of Turkey Electricity Energy Demand. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 142–152. https://doi.org/10.18201/ijisae.2022.278

Zahid, M., Ahmed, F., Javaid, N., Abbasi, R. A., Kazmi, H. S. Z., Javaid, A., … Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics, 8(2), 122. https://doi.org/10.3390/electronics8020122