Abstract
Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract useful information from the electricity price time series. To be precise, two clustering techniques, K-means and Expectation Maximization, have been utilized for the analysis of the prices curve, demonstrating that the application of these techniques is effective so to split the whole year into different groups of days, according to their prices conduct. Later, this information will be used to predict the price in the short time period. The prices exhibited a remarkable resemblance among days embedded in a same season and can be split into two major kind of clusters: working days and festivities.
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.
Similar content being viewed by others
References
Amjady, N.: Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Transactions on Power Systems 21(2), 887–896 (2006)
Conejo, A.J., Plazas, M.A., Espńnola, R., Molina, B.: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transactions on Power Systems 20(2), 1035–1042 (2005)
Cramér, H.: Mathematical methods of statistics. Princeton Univ. Press (1946)
García, R.C., Contreras, J., van Akkeren, M., García, J.B.: A GARCH forecasting model to predict day-ahead electricity prices. IEEE Transactions on Power Systems 20(2), 867–874 (2005)
García-Martos, C., Rodríguez, J., Sánchez, M.J.: Mixed models for short-run forecasting of electricity prices: Application for the spanish market. IEEE Transactions on Power Systems 22(2), 544–552 (2007)
Guha, S., Rastogi, R., Shim, K.: A framework for electricity price spike analysis with advanced data mining methods. IEEE Transactions on Power Systems 22(1), 376–385 (2007)
Kaufman, L., Rousseeuw, P.J.: Finding groups in Data: an Introduction to Cluster Analysis. Wiley, Chichester (1990)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 1137–1143 (1995)
Lauritzen, S.L.: The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis 19(2), 191–201 (1995)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statististics and Probability, pp. 281–297 (1968)
Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C., Riquelme, J.M.: Discovering patterns in electricity prices using clustering techniques. In: Proceedings of the International Conference on Renewable Energies and Power Quality (2007)
Spanish Electricity Price Market Operator, http://www.omel.es
Plazas, M.A., Conejo, A.J., Prieto, F.J.: Multimarket optimal bidding for a power producer. IEEE Transactions on Power Systems 20(4), 2041–2050 (2005)
Troncoso, A., Riquelme, J.C., Riquelme, J.M., Martínez, J.L., Gómez, A.: Electricity market price forecasting based on weighted nearest neighbours techniques. IEEE Transactions on Power Systems 22(3), 1294–1301 (2007)
Xu, R., Wunsch II., D.C.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Zacks, S.: The theory of statistical inference. Wiley, Chichester (1946)
Zareipour, H., Bhattacharya, K., Cañizares, C.A.: Forecasting the hourly Ontario energy price by multivariate adaptive regression splines. IEEE Transactions on Power Systems 20(2), 1035–1042 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C., Riquelme, J.M. (2007). Partitioning-Clustering Techniques Applied to the Electricity Price Time Series. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_99
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)