Identification of Residential Energy Consumption Behaviors

Abstract

Climate change has raised consciousness of the need to use cleaner energy instead of fossil fuels. Meanwhile, a change of consciousness regarding resource use has to be achieved, which can be triggered by energy consumption monitoring studies that also provide useful recommendations for energy saving. Over time, researchers have identified behaviors by monitoring energy consumption in households, but these studies are usually limited to the number of monitored households and/or to the geographical region in which the monitoring takes place. In this research work, a study with a global reach is proposed to mitigate these limitations. Using a hierarchical clustering algorithm, three distinct groups were identified using the collected data, representative of distinct behaviors. The results illustrate several behaviors regarding energy consumption, like cold temperatures, seasonal behaviors, wake up hour, stay-at-home periods, and standby device consumption.