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Partitioning-Clustering Techniques Applied to the Electricity Price Time Series

Opened Access Partitioning-Clustering Techniques Applied to the Electricity Price Time Series

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Autor: Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Riquelme Santos, Jesús Manuel
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2007
Publicado en: Intelligent Data Engineering and Automated Learning - IDEAL 2007, Lecture Notes in Computer Science, Volume 4881 pp 990-999 (2007)
Tipo de documento: Capítulo de Libro
Resumen: 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.
Tamaño: 403.4Kb
Formato: PDF

URI: http://hdl.handle.net/11441/39731

DOI: http://dx.doi.org/10.1007/978-3-540-77226-2_99

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