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 first results of an interdisciplinary research project which involves researchers in energy, meteorology, and data mining, addressing this real-world problem. In Reunion Island from December 2008 to March 2012, solar radiation measurements have been collected, every minute, using calibrated instruments. Prior to prediction modelling, two clustering strategies have been applied for the analysis of the data base of 951 days. The first approach combines the following proven data-mining methods. principal component analysis (PCA) 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 clustering method that operates on a set of dissimilarity matrices. Each cluster is represented by an element or a subset of the set of objects to be classified. The five meaningfully clusters found by the two clustering approaches are compared. The interest and disadvantages of the two approaches for classifying curves are discussed.
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
References
de Carvalho, F. A. T., Csernel, M., & Lechevallier, Y. (2009). Clustering constrained symbolic data. Pattern Recognition Letter, 30, 1037–1045.
de Carvalho, F. A. T., Lechevallier, Y., & De Melo, F. M. (2012). Partitioning hard clustering algorithms based on multiple dissimilarity matrices. Pattern Recognition, 45, 447–464.
Diday, E., & Govaert, G. (1977). Classification automatique avec distances adaptatives. R.A.I.R.O. Informatique Computer Science, 11(4), 329–349.
D’urso, P., & Vichi, M. (1998). Dissimilarities between trajectories of a three-way longitudinal data set. In A. Rizzi, M. Vichi, & H.-H. Bock (Eds.), Advances in data science and classification (pp. 585–592). Berlin: Springer.
Frigui, H., Hwang, C., & Rhee, F. (2007). Clustering and aggregation of relational data with applications to image database categorization. Pattern Recognition, 40, 3053–3068.
Hathaway, R. J., & Bezdek, J. C. (1994). Nerf c-means: Non-Euclidean relational fuzzy clustering. Pattern Recognition, 27(3), 429–437.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computer Survey, 31(3), 264–323.
Jeanty, P., Delsaut, M., Trovalet, L., Ralambondrainy, H., Lan-sun-luk, J. D., Bessafi, M., Charton, P., & Chabriat, J. P. (2013). Clustering daily solar radiation from reunion island using data analysis methods. In International Conference on Renewable Energies and Power Quality. Spain: Bilbao.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data. New York: Wiley.
Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R package for multivariate analysis. Journal of Statistical Software, 25(1), 1–18.
Milligan, G. W., & Cooper, M. C. (1996). Clustering validation: Results and implications for applied analysis. In P. Arabie, L. Hubert, & G. De Soete (Eds.), Clustering and classification. (pp. 341–375). Singapore: World Scientific.
Muselli, M., Poggi, P., Notton, G., & Louche, A. (2000). Classification of typical meteorological days from global irradiation records and comparison between two mediterranean coastal sites in corsica island. Energy Conversion Management, 41, 1043–1063.
Soudhan, T., Emilion, R., & Calif, R. (2009). Classification of daily solar radiation distributions using a mixture of Dirichlet distributions. Solar Energy, 83, 1056–1063.
Acknowledgements
This work received financial support from Europe, Regional Reunion Island Council and the French government through the ERDF (European Regional Development Fund) and ADEME (French Environment and Energy Management Agency).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bessafi, M. et al. (2015). Clustering of Solar Irradiance. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds) Data Science, Learning by Latent Structures, and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44983-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-44983-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44982-0
Online ISBN: 978-3-662-44983-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)