Office Occupancy Detection based on Power Meters and BLE Beaconing

Energy consumption for both residential and non-residential buildings is significant and has been increasing regularly. For non-residential buildings, asking the user to be directly involved in energy saving can be challenging as occupants (e.g., employees) are less aware of and affected by high energy bills compared to their domestic situation. Employees are less careful when leaving empty office spaces heated and illuminated, resulting in unnecessary energy consumption. This thesis focuses on finding solutions for solving energy waste in non-residential buildings by automatically detecting the presence, thus enabling energy-saving automation.To reduce energy consumption due to unnecessary use, precise and detailed user contexts play an important role. User contexts (e.g., occupancy and activity of users) provide grounds to buildings’ control and energy management systems for efficient lighting and HVAC actuation. We explore sensing systems that indicate occupancy. Namely, we extract occupancy from power consumption (i.e., power metering or sub-metering systems) and proximity location (i.e., mobile phones with beaconing systems). We investigate several strategies and machine learning algorithms to infer occupancy from these sources. We also study fusions at the decision-level and feature-level. The former allows sub-systems to infer local decisions and finally combines the outputs to form a final decision. The latter yields only decision after sensor readings have been combined. The approaches are tested in actual office environments populated by researchers and software developers. We finally discuss potential energy saving, user privacy, and portability to provide insight into how the proposed occupancy detection systems may impact building use and control

Indoor self-localization via bluetooth low energy beacons

Indoor localization is concerned with mapping sensory data to physical locations inside buildings. Location of a user or a mobile device is an essential part of the context, and is therefore very useful for pervasive computing applications. Many proposals exist for solving the localization problem, typically based on image or radio signal processing, though the problem is still generally considered to be open, especially when costs and privacy constraints play an important role. In this paper, we propose a solution based on the emerging Bluetooth Low Energy (BLE) standard and off-the-shelf hardware. Such approach proves to satisfy economic constraints, while challenging in terms of accurate location. To translate beacon signals into locations, we consider several approaches, i.e., cosine similarity, nearest neighbourhood classification, and the nearest beacon. Our experiments indicate a vector based approach as the most suited one. In fact, we show its effectiveness in an actual office deployment consisting of five indoor areas: three multiuser offices, a social corner, and a hallway. We achieve 90% and 80% for accuracy and F-measure, respectively

Office Low-Intrusive Occupancy Detection Based on Power Consumption

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

Low-power Appliance Recognition using Recurrent Neural Networks

Indoor energy consumption can be understood by breaking overall power consumption down into individual components and appliance activations. The clas- sification of components of energy usage is known as load disaggregation or ap- pliance recognition. Most of the previous efforts address the separation of devices with high energy demands. In many contexts though, such as an office, the devices to separate are numerous, heterogeneous, and have low consumptions. The disag- gregation problem becomes then more challenging and, at the same time, crucial for understanding the user context. In fact, from the disaggregation one can deduce the number of people in an office room, their activities, and current energy needs. In this paper, we review the characteristics of office appliances load disaggregation efforts. We then illustrate a proposal for a classification model based on Recur- rent Neural Network (RNN). RNN is used to infer device activation from aggre- gated energy consumptions. The approach shows promising results in recognizing 14 classes of 5 different devices being operated in our office, reaching 99.4% of Cohen’s Kappa measure