A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns

Existing building energy ratings are typically derived with the annual average energy consumption of the buildings. This approach may be appropriate for formulating community-level energy strategy at the macro level, but it cannot be directly linked to occupant behavior for energy savings at the micro level. In light of this, this study aimed to propose a novel process model for developing a scalable room-level energy benchmark using real-time bigdata, which focused on identifying representative energy usage patterns and encouraging occupant behavior change for energy savings. When creating a scalable room-level energy benchmark, three views were taken into account: (i) space unit as perceived by occupants, for which space-specific energy usage datasets were classified based on space attributes; (ii) time unit to which occupants can respond simultaneously, for which hourly energy usage datasets were used; and (iii) equipment unit to which occupants can precisely respond, for which energy usage datasets by different types of electrical installation and appliance were utilized. Based on the scalable room-level energy benchmark, the main findings can be summarized: (i) five representative energy usage patterns were identified using k-means clustering method; (ii) the year-round distributions of the five representative patterns were investigated by month and weekday; and (iii) the annual average variance (or uncertainty) of the room-level scalable energy benchmark was determined to be 19.6%. By providing spatio-temporal information on energy usage patterns in real time, it is expected that occupant behavior change can be voluntarily encouraged to save energy in buildings using the proposed approach.