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Unsupervised clustering of battery waveforms in off-grid PV installations

This paper introduces a new methodology to classify data extracted from off-grid solar installations, based on the charge and discharge processes. Using data from a real installation, we propose to classify the daily behaviour of the energy harvesting according to characteristics extracted from the voltage and current curves of the installation. Five type situations have been established, in which the off-grid system charge can be summarized. This classification can help to better understand the state of the installations and classify the behaviour of the charging process to train more complex algorithms.