Characterizing Thermal Energy Consumption through Exploratory Data Mining Algorithms

Nowadays large volumes of energy data are continuously collected through a variety of meters from dierent smart-city environments. Such data have a great potential to influence the overall energy balance of our communities by optimizing building energy consumption and by enhancing people's awareness of energy wasting. This paper presents FARTEC, a data mining engine based on exploratory and unsupervised data mining algorithms to characterize building energy consumption together with meteorological conditions. FARTEC exploits a joint approach coupling cluster analysis and association rules. First, a partitional clustering algorithm is applied to weather conditions to discover groups of thermal energy consumption that occurred in similar weather conditions. Each computed cluster is then locally characterized through a set of association rules to ease the manual inspection of the most interesting correlations between thermal consumption and weather conditions. FARTEC also includes a categorization of the rules into a few groups according to their meaning. Each group is determined by the data features appearing in the rule. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in discovering interesting knowledge items to raise people's awareness of their energy consumption.