Data Sensing Approaches to Monitoring Building Energy Use and Occupant Behavior

Building occupant behavior impacts energy consumption but this is often not adequately captured when monitoring energy use. It is important to provide indoor environmental conditions that are well suited to occupant needs and promote their well-being while minimizing energy wastage. Machine learning methods have been employed to predict energy consumption but in the absence of adequate occupant-related monitoring data, it is challenging to establish the right inputs and obtain predictions that reflect occupant characteristics (behavior and preferences). This paper presents the approaches employed to capture occupant behavior-related energy data using a combination of sensors, metering devices, and surveys to continuously track space conditions, occupant behavior, and energy consumption over a period of time. Two case study buildings are selected for this study, some of the challenges with energy monitoring are identified, and the need for interoperability among different sensors is discussed.