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Published in:
2007 | OriginalPaper | Chapter
Partitioning-Clustering Techniques Applied to the Electricity Price Time Series
Authors : F. Martínez-Álvarez, A. Troncoso, J. C. Riquelme, J. M. Riquelme
Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Publisher: Springer Berlin Heidelberg
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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.