Abstract
Liberalized electricity market players all over the world face a significant challenge due to the volatile and uncertain nature of these markets. Therefore, price and volatility forecasting in those markets are as remarkably of interest as other commodity markets. There exists a recent and increasing tendency in the literature to apply machine-learning methodologies to those markets’ data and various methods have been proven effective to produce highly accurate forecasts. The Turkish electricity market is one of the recently liberalized and emerging markets. In this research, we aim to carry out price and volatility forecasting for the Turkish day-ahead electricity market with Support Vector Regression (SVR) and Random Forest (RF) to observe the effectiveness of the methods. A rolling forecasting scheme is proposed and experimented with using hourly prices between 2013 and 2019. The performance metrics of the SVR model are compared with those of naive and RF estimations. Furthermore, the sensitivity of the proposed model to feature reduction is also discussed. Overall, the results reveal SVR as an effective tool for electricity price forecasting in the Turkish electricity market, whereas RF modeling is found to be slightly better in volatility forecasting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
References
Aggarwal, S. K., Saini, L. M., & Kumar, A. (2009). Day-ahead price forecasting in Ontario electricity market using variable-segmented support vector machine-based model. Electric Power Components and Systems, 37(5), 495–516.
Amjady, N., & Farshid, K. (2009). Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique. Energy Conversion and Management, 50, 2976–2982.
Armstrong, J. S. (1985). Long-range forecasting: From crystal ball to computer (2nd Ed.). Wiley.
Aydogdu, E. (2016). Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis. Energy Economics, 56, 398–408.
Avci, E., Ketter, W., & Heck, E. V. (2018). Managing electricity price modeling risk via ensemble forecasting: The case of Turkey. Energy Policy, 123, 390–403.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Brusaferri, A., Matteucci, M., Portolani, P., & Vitali, A. (2019). Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices. Applied Energy, 250, 1158–1175.
Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing, 361, 151–163.
Che, J., & Wang, J. (2010). Short-term electricity prices forecasting based on support vector regression and auto-regressive integrated moving average modeling. Energy Conversion and Management, 51, 1911–1917.
Cheng, H., Ding, X., Zhou, W., & Ding, R. (2019). A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange. Electrical Power and Energy Systems, 110, 653–666.
Debnath, K. B., & Mourshed, M. (2018). Forecasting methods in energy planning models. Renewable and Sustainable Energy Reviews, 88, 297–325.
Fan, S., Mao, C., & Chen, L. (2007). Next-day electricity-price forecasting using a hybrid network. IET Generation, Transmission & Distribution, 1(1), 176–182.
Frömmel, M., Han, X., & Kratochvil, S. (2014). Modeling the daily electricity price volatility with realized measures. Energy Economics, 44, 492–502.
Gao, C., Bompard, E., Napoli, R., & Cheng, H. (2007). Price forecast in the competitive electricity market by support vector machine. Physica A: Statistical Mechanics and Its Applications, 382(1), 98–113.
Gayretli, G., Yucekaya, A., & Bilge, A. H. (2019). An analysis of price spikes and deviations in the deregulated Turkish market power market. Energy Strategy Reviews, 26, 100376.
Ghayekhloo, M., Azimi, R., Ghofrani, M., Menhaj, M. B., & Shekari, E. (2019). A combination approach based on a novel data clustering method and Bayesian recurrent neural network for day-ahead price forecasting of electricity markets. Electric Power Systems Research, 168, 184–199.
Ghoddusi, H., Creamer, G. G., & Rafizadeh, N. (2019). Machine learning in energy economics and finance: A review. Energy Economics, 81, 709–727.
González, C., Mira, J. M., & Ojeda, J. A. (2016). Applying multi-output random forest models to electricity price forecast. Preprints.org, 2016090053.
Haugom, E., & Ullrich, C. J. (2012). Forecasting spot price volatility using the short-term forward curve. Energy Economics, 34(6), 1826–1833.
Hayfavi, A., & Talasli, I. (2014). Stochastic multifactor modeling of spot electricity prices. Journal of Computational and Applied Mathematics, 259, 434–442.
Keles, D., Scelle, J., Paraschiv, F., & Fichtner, W. (2016). Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Applied Energy, 162, 218–230.
Kolmek, 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 & Computer Sciences, 23, 841–852.
Lago, J., De Ridder, F., Vrancx, P., & De Schutter, B. (2018). Forecasting day-ahead electricity prices in Europe: The importance of considering market integration. Applied Energy, 211, 890–903.
Li, X. R., Yu, C. W., Ren, S. Y., Chiu, C. H., & Meng, K. (2013). Day-ahead electricity price forecasting based on panel cointegration and particle filter. Electric Power Systems Research, 95, 66–76.
Luo, S., & Weng, Y. (2019). A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources. Applied Energy, 242, 1497–1512.
Marcjasz, G., Uniejewski, B., & Weron, R. (2019). On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks. International Journal of Forecasting, 35(4), 1520–1532.
Mei, J., He, D., Harley, R., Habetler T., & Qu, G. (2014). A random forest method for real-time price forecasting in New York electricity market. In 2014 IEEE PES General Meeting|Conference & Exposition, National Harbor, MD (pp. 1–5).
Ozguner, E., Tor, O. B., & Guven, A. N. (2017). Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market. Turkish Journal of Electrical Engineering & Computer Sciences, 25, 4923–4935.
Ozozen, A., Kayakutlu, G., Ketterer, M., & Kayalica, O. (2016). A combined seasonal ARIMA and ANN model for improved results in electricity spot price forecasting: Case study in Turkey. In Proceedings of the 2016 Portland International Conference on Management of Engineering and Technology (PICMET), Honolulu, HI, USA (pp. 2681–2690).
Qu, H., Chen, W., Niu, M., & Li, X. (2016). Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models. Energy Economics, 54, 68–76.
Qu, H., Duan, Q., & Niu, M. (2018). Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets. Energy Economics, 74, 767–776.
Saini, L. M., Aggarwal, S. K., & Kumar, A. (2009). Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market. IET Generation, Transmission & Distribution, 4(1), 36–49.
Sandhu, H. S., Fang, L., & Guan, L. (2016). Forecasting day-ahead price spikes for the Ontario electricity market. Electric Power Systems Research, 141, 450–459.
Shayeghi, H., & Ghasemi, A. (2013). Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme. Energy Conversion and Management, 74, 482–491.
Tashpulatov, S. N. (2013). Estimating the volatility of electricity prices: The case of the England and Wales wholesale electricity market. Energy Policy, 60, 81–90.
Ugurlu, U., Oksuz, I., & Tas, O. (2018a). Electricity price forecasting using recurrent neural networks. Energies, 11(5), 1255.
Ugurlu, U., Tas, O., & Gunduz, U. (2018b). Performance of electricity price forecasting models: Evidence from Turkey. Emerging Markets Finance and Trade, 54(8), 1720–1739.
Vapnik, V. (1995). The nature of statistical learning theory. Springer.
Wang, F., Li, K., Zhou, L., Ren, H., Contreras, J., Shafie-Khah, M., & Catalão, J. P. (2019). Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting. International Journal of Electrical Power & Energy Systems, 105, 529–540.
Yang, W., Wang, J., Niu, T., & Du, P. (2019). A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Applied Energy, 235, 1205–1225.
Zhang, J. L., Zhang, Y. J., Li, D. Z., Tan, Z. F., & Ji, J. F. (2019). Forecasting day-ahead electricity prices using a new integrated model. International Journal of Electrical Power & Energy Systems, 105, 541–548.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kara, M., Atici, K.B., Ulucan, A. (2021). Price and Volatility Forecasting in Electricity with Support Vector Regression and Random Forest. In: Dorsman, A.B., Atici, K.B., Ulucan, A., Karan, M.B. (eds) Applied Operations Research and Financial Modelling in Energy. Springer, Cham. https://doi.org/10.1007/978-3-030-84981-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-84981-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84980-1
Online ISBN: 978-3-030-84981-8
eBook Packages: Business and ManagementBusiness and Management (R0)