A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data

Arief Ang, I, Hamilton, M and Salim, F 2018, 'A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data', ACM Transactions on Sensor Networks, vol. 14, no. 3-4, pp. 1-28.


Document type: Journal Article
Collection: Journal Articles

Title A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data
Author(s) Arief Ang, I
Hamilton, M
Salim, F
Year 2018
Journal name ACM Transactions on Sensor Networks
Volume number 14
Issue number 3-4
Start page 1
End page 28
Total pages 28
Publisher Association for Computing Machinery
Abstract Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter Plus Plus (DA-HOC++), a robust way to estimate the number of people within one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the training and labelled data are adequately available. DA-HOC++ is able to predict the number of occupants with minimal training data: as little as 1 day's data. DA-HOC++ accurately predicts indoor human occupancy for five different rooms across different countries using a model trained from a small room and adapted to other rooms. We evaluate DA-HOC++ with two baseline methods: a support vector regression technique and an SD-HOC model. The results demonstrate that DA-HOC++'s performance on average is better by 10.87% in comparison to SVR and 8.65% in comparison to SD-HOC.
Subject Pattern Recognition and Data Mining
Ubiquitous Computing
Keyword(s) Transfer learning
Domain adaptation
Human occupancy count prediction
Ambient sensing
Machine learning
Building occupancy
Presence detection
Number estimation
Crossspace modeling
Contextual information
DOI - identifier 10.1145/3217214
Copyright notice © 2018 Association for Computing Machinery
ISSN 1550-4859
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