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The increasing application of smart and digital energy meters leads to an increasing availability of frequent -e.g. hourly- and long-term measurements of the actual energy use in occupied buildings. In the resulting energy use time series, the diurnal fluctuations in energy use are recognised and similarities between diurnal profiles for various days are observed. These recurring profiles are called energy use time patterns and they are a result of various phenomena, such as patterns in the building use, occupational schedules, settings of the system control, short-term weather dynamics etc. These energy use time patterns can provide a better understanding of the energy use, which is useful in many fields including energy feedback, fault detection and energy auditing. In order to identify and characterise energy use time patterns for large data-sets, an automated approach is needed. This paper proposes a methodology for automated mathematical recognition of energy use time patterns based on cluster analysis. Secondly, a methodology to characterise the identified patterns in function of external variables is proposed, using classification analysis techniques. The methodologies allow an automated identification and characterization of energy use time patterns, allowing a better understanding of the variations and changes in building energy use and their relation to weather conditions and calendar aspects.

Identification of Energy Use Time Patterns of Occupied Dwellings using Smart Meter Data