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access icon free Designing learned CO2-based occupancy estimation in smart buildings

© The Institution of Engineering and Technology
Received 31/01/2018, Accepted 29/08/2018, Revised 17/08/2018, Published 30/08/2018

Many applications, such as smart buildings, crowd flow, action recognition, and assisted living, rely on occupancy information. Although the use of smart cameras and computer vision can assist with these tasks and provide accurate occupancy information, it can be cost prohibitive, invasive, and difficult to scale or generalise to different environments. An alternative solution should bring similar accuracy while minimising the listed problems. This work demonstrates that a scalable wireless sensor network with CO2-based estimation is a viable alternative. To support many applications, a solution must be transferable and must handle not knowing the physical system model; instead, it must learn to model CO2 dynamics. This work presents a viable prototype and uses the captured data to train machine learning-based occupancy estimation systems. Models are trained under varying conditions to assess the consequences of design decisions on performance. Four different learning models were compared: gradient boosting, k-nearest neighbours (KNN), linear discriminant analysis, and random forests. With sufficient labelled data, the KNN model produced peak results with a root-mean-square error value of 1.021.

Inspec keywords: computer vision; mean square error methods; building management systems; learning (artificial intelligence); nearest neighbour methods; image sensors; wireless sensor networks

Other keywords: accurate occupancy information; root-mean-square error value; physical system model; CO2; machine learning-based occupancy estimation systems; CO2-based estimation; linear discriminant analysis; computer vision; viable prototype; learning models; viable alternative; alternative solution; action recognition; random forests; design decisions; smart cameras; smart buildings; scalable wireless sensor network; listed problems; KNN model; KNN; crowd flow

Subjects: Control engineering computing; Interpolation and function approximation (numerical analysis); Image sensors; Computer vision and image processing techniques; Optical, image and video signal processing; Automated buildings; Interpolation and function approximation (numerical analysis); Wireless sensor networks; Other topics in statistics; Knowledge engineering techniques; Other topics in statistics

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