Density-based spatial clustering of application with noise algorithm for the classification of solar radiation time series

摘要

The study of the dynamic behaviour of the solar radiation is a very important task for PV system efficiency. Hence, we propose in this paper, a time series data mining method to detect the underlying dynamic presents in hourly solar radiation time series. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster the solar radiation time series and detect noisy data. Moreover, the proposed method is compared with two unsupervised clustering techniques, k-means and Fuzzy c-means, for the analysis of the measured hourly solar radiation time series. All the algorithms are focused on extracting useful information from the data with the aim of model the time series behaviour and find patterns to be used in PV system applications. This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.