Analysis of Delay Interval and Energy-Load Variation for Non-Intrusively Extracting Occupant Energy-Use Information in Commercial Buildings

Many studies indicate that energy consumption in commercial buildings is highly related to occupants’ energy-use behaviors, and improving these behaviors is regarded as the most cost-effective approach toward enhancing commercial building energy efficiency. Effective behavior-interventions rely on the availability of occupant-specific energy-use information, which is extremely expensive to capture with existing intrusive load monitoring (ILM) technologies. On the other hand, non-intrusive load monitoring (NILM) approaches have proven cost effective for monitoring appliance-specific energy consumption. In order to extend the concept of NILM to occupant energy-use monitoring in commercial buildings, this paper examines the importance of two occupancy-related energy-use variables—delay interval and energy-load variation—in identifying occupant-specific energy-use information. The results from implementing a k-Nearest Neighbors classifier into aggregate energy consumption data collected over the course of one month from a small office space reveal that these variables are effective in developing sophisticated NILM-based approaches for obtaining occupant energy consumption information. By providing this information at minimal cost, such approaches could make a great contribution to behavior-related energy research.