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

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.