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.