Abstract
This paper presents customized models to predict next-day’s electricity price in short-term periods for Turkey’s electricity market. Turkey’s electricity market is evolving from a centralized approach to a competitive market. Fluctuations in the electricity consumption show that there are three periods; day, peak, and night. The approach proposed here is based on robust and continuous optimization techniques, which ensures achieving the optimum electricity price to minimize error in periodic price prediction. Commonly, next-day’s electricity prices are forecasted by using time series models, specifically dynamic regression model. Therefore electricity price prediction performance was compared with dynamic regression. Numerical results show that CMARS and RCMARS predicts the prices with 30% less error compared to dynamic regression.
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
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, S., Saini, L.M., Kumar, A.: Electricity price forecasting in Ontario electricity market using wavelet transform in artificial neural network based model. Int. Journal of Control, Automation, and Systems. 6, 639–650 (2008)
Ben-Tal, A., Nemirovski, A.: Lectures on Modern Convex Optimization: Analysis, Algorithms and Engineering Applications, MPPR-SIAM Series on Optimization. SIAM,Philadelphia (2001)
Gonzalez, A.M., Roque, A.M.S., Garcia-Gonzalez, J.: Modelling and forecasting electricity prices with input/output hidden Markov models. IEEE Trans. Power Syst. 20, 13–24 (2005)
Gujarati, D. N., Porter, D. C.: Basic Econometrics. McGraw-Hill, Boston (2009)
Nogales, F. J., Contreras, J., Conejo, A. J., Espinola, R.: Forecasting next-day electricity prices by time series models. IEEE Trans. Power Syst. 17, 342–348 (2002)
Weber, G. W., Batmaz, ˙I., K¨oksal, G., Taylan, P., Yerlikaya, F.: C-MARS: A new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. to appear in Inverse Problems in Science and Engineering (IPSE)
¨Ozmen, A.: Robust Conic Quadratic Programming Applied to Quality Improvement- A Robustification of CMARS. Master Thesis. METU, Ankara, Turkey (2010)
Zareipour, H., Janjani, A., Leung, H.,Motamedi, A., Schellenberg, A.: Classification of future electricity market prices. IEEE Trans. Power Syst. 26, 165–173 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yıldırım, M.H., Özmen, A., Bayrak, Ö.T., Weber, G.W. (2012). Electricity Price Modelling for Turkey. In: Klatte, D., Lüthi, HJ., Schmedders, K. (eds) Operations Research Proceedings 2011. Operations Research Proceedings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29210-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-29210-1_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29209-5
Online ISBN: 978-3-642-29210-1
eBook Packages: Business and EconomicsBusiness and Management (R0)