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We explore different methods of analyzing large and complex datasets related to building-integrated photovoltaics (BIPV). We use the data of the European RESSOURCES project obtained from ETNA, an experimental setup consisting of two full-scale replicas of residential homes featuring a double-skin facade. We show that classic data mining methods such as mutual information can be used to gain a better understanding of the physics behind BIPV systems and to highlight discrepancies between different experimental setups. We then use artificial neural networks to model the airflow inside a double-skin facade and quantify its contribution to the cooling and heating of buildings.

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