This paper presents a new data mining engine, named EXTREMA (EXploitation of Turin high Resolution Energy MAps), to automatically visualise high-resolution energy maps exploring interesting and human-readable knowledge items from large collections of EPCs. EXTREME , developed in extbf{Python}, generates geo-located maps to summarise the main relationships among variables affecting the energy efficiency of buildings at different spatial granularity levels. The visualised knowledge is discovered through a two-level data analytics methodology based on exploratory and unsupervised algorithms. First an unsupervised algorithm divides EPCs into homogeneous groups of buildings with similar thermo-physical characteristics. Each group is then locally characterised through interesting patterns to concisely represent each group.

Visualising high-resolution energy maps through the exploratory analysis of energy performance certificates / Cerquitelli, T.; Di Corso, E.; Proto, S.; Capozzoli, A.; Mazzarelli, D.; Nasso, A.; Baralis, E.; Mellia, M.; Casagrande, S.; Tamburini, M.. - ELETTRONICO. - (2019), pp. 1-6. ((Intervento presentato al convegno 2nd International Conference on Smart Energy Systems and Technologies, SEST 2019 tenutosi a Porto (Portugal) nel 2019 [10.1109/SEST.2019.8849061].

Visualising high-resolution energy maps through the exploratory analysis of energy performance certificates

Cerquitelli T.;Di Corso E.;Capozzoli A.;Baralis E.;Mellia M.;
2019

Abstract

This paper presents a new data mining engine, named EXTREMA (EXploitation of Turin high Resolution Energy MAps), to automatically visualise high-resolution energy maps exploring interesting and human-readable knowledge items from large collections of EPCs. EXTREME , developed in extbf{Python}, generates geo-located maps to summarise the main relationships among variables affecting the energy efficiency of buildings at different spatial granularity levels. The visualised knowledge is discovered through a two-level data analytics methodology based on exploratory and unsupervised algorithms. First an unsupervised algorithm divides EPCs into homogeneous groups of buildings with similar thermo-physical characteristics. Each group is then locally characterised through interesting patterns to concisely represent each group.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2787056