Clustering of Solar Irradiance

Abstract : The development of grid-connected photovoltaic power systems leads to new challenges. The short or medium term prediction of the solar irradiance is definitively a solution to reduce the storage capacities and, as a result, authorizes to increase the penetration of the photovoltaic units on the power grid. We present the firts results of an interdisciplinary research project which involves researchers in energy, meteorology and data mining, adressing this real-world problem. In Reunion Island from December 2008 to March 2012, solar radiation measurements has been collected, every minutes, using calibrated instruments. Prior to prediction modelling, two clustering strategies has been applied for analysing the data base of 951 days. The first approach combines the following proven data-mining methods. Principal Component Analysis was used as a pre-process for reduction and denoising and the Ward Hierarchical and K-means methods to find a partition with a good number of classes. The second approach uses a method based on (De Carvalho et al., 2012). This clustering method operates on a set of dissimilarity matrices. Each cluster is represented by an element of the set of objects to be classified. The five meangfully clusters found by the two clustering approaches are compared. The interest and disadvantages of the two approaches for classifying curves are discussed.
Type de document :
Communication dans un congrès
European Conference on Data Analysis, Jul 2013, Luxembourg, Luxembourg. Springer, 2013, Studies in Classification, Data Analysis, and Knowledge Organization
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https://hal.inria.fr/hal-00944874
Contributeur : Thierry Despeyroux <>
Soumis le : mardi 11 février 2014 - 13:33:07
Dernière modification le : mardi 31 juillet 2018 - 15:04:02

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  • HAL Id : hal-00944874, version 1

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Miloud Bessafi, Francisco De Carvalho, Philippe Charton, Mathieu Delsaut, Patrick Jeanty, et al.. Clustering of Solar Irradiance. European Conference on Data Analysis, Jul 2013, Luxembourg, Luxembourg. Springer, 2013, Studies in Classification, Data Analysis, and Knowledge Organization. 〈hal-00944874〉

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