Using smart technologies to identify occupancy and plug-in appliance interaction patterns in an office environment

There has been a widely documented phenomenon identifying an energy performance gap between the building's design and operational phases. This result has been attributed to the stochastic behaviours exhibited by the occupants, who are assumed to follow deterministic and routine schedules. With the recent advancements in smart monitoring technologies, the increasing affordability of wireless sensors has allowed researchers to collect detailed information on the occupants' dynamic behaviours. However, past applications of such technologies have been highly intrusive and limit the validity of the data collected due to the Hawthorne effect. Therefore, this paper proposes a non-intrusive data collection methodology using a comprehensive range of wireless smart meters, Bluetooth beacons, and questionnaires to capture the occupants' movement and appliance interaction patterns. The feasibility of the approach is demonstrated during a two-week data collection effort in a university office. By combining the occupants' presence with appliance energy consumption data, the authors were able to identify the occupants' appliance interaction patterns. An extension of this work includes the use of the data collected to identify different occupancy and appliance interaction profiles, which contributes to the development of an appliance interaction model that addresses the energy performance gap caused by occupants' appliance interaction.