Office Low-Intrusive Occupancy Detection Based on Power Consumption

Abstract

Precise fine-grained office occupancy detection can be exploited for energy savings in buildings. Based on such information one can optimally regulate lighting and climatization based on the actual presence and absence of users. Conventional approaches are based on movement detection, which are cheap and easy to deploy, but are imprecise and offer coarse information. We propose a power monitoring system as a source of occupancy information. The approach is based on sub-metering at the level of room circuit breakers. The proposed method tackles the problem of indoor office occupancy detection based on statistical approaches, thus contributing to building context awareness which, in turn, is a crucial stepping stone for energy-efficient buildings. The key advantage of the proposed approach is to be low intrusive, especially when compared with image- or tag-based solutions, while still being sufficiently precise in its classification. Such classification is based on nearest neighbors and neural networks machine learning approaches, both in sequential and non-sequential implementations. To test the viability, precision, and saving potential of the proposed approach we deploy in an actual office over several months. We find that the room-level sub-metering can acquire precise, fine-grained occupancy context for up to three people, with averaged kappa measures of 93-95% using either the nearest neighbors or neural networks based approaches.

Keywords

• Context-awareness
• load disaggregation
• occupancy detection
• power monitoring
• sensor systems and applications
• smart metering