Extracting energy-related knowledge from mining occupants’ behavioral data in residential buildings

Abstract

In this study, a data mining-based framework is developed to provide practical insight into occupants' energy behavior in residential buildings. Occupant Activity Indicator (OAI) and Residential Energy Intensity Indicator (REII) are introduced as two new indicators. The proposed data-mining framework aims to evaluate the energy-related behavior of the buildings' residents based on the difference between the target REII and actual REII. The dissimilarity, which is found between the target and the actual REII, is used to identify when occupants behave wasteful/efficient in different zones of the building. The practicality of the proposed methodology is demonstrated by applying it to a three-bedroom apartment. As a result, the time and zones for potential energy savings in the considered building are identified. The extracted knowledge identifies the zones that occupants need to be more cautious about their energy consumption. Besides, location and time for the improvement in energy-related behavior of the occupants are defined. Further, the variations of occupants' energy-related behavior in the apartment are identified by the time of day, day of the week, and months.