Identifying power profiles in the photovoltaic power station data by self-organizing maps and dimension reduction by Sammon´s projection

This paper presents results of the identification of clusters in the hourly recorded data of power from a small photovoltaic power station. Our main aim was to find a method of how to identify typical patterns of generated power. Although one can think that sunny days are the same, the power of the sun light is very volatile during a day. We were not interested in finding the absolute values of this power but just its patterns according to the day´s maximal power. Our proposed method is based on several techniques. We used network algorithm as a method for removing noise from the data, Sammon´s projection for visualization and dimensionality reduction and final clustering by the self-organizing maps.