DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data

Arief Ang, I, Salim, F and Hamilton, M 2017, 'DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data', in Rasit Eskicioglu (ed.) Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2017), Delft, Netherlands, 8-9 November 2017, pp. 1-10.


Document type: Conference Paper
Collection: Conference Papers

Title DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data
Author(s) Arief Ang, I
Salim, F
Hamilton, M
Year 2017
Conference name BuildSys 2017
Conference location Delft, Netherlands
Conference dates 8-9 November 2017
Proceedings title Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2017)
Editor(s) Rasit Eskicioglu
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 1
End page 10
Total pages 10
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 (DA-HOC), a robust way to estimate the number of people within in 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 occupancy with minimal training data, as little as one-day data. DA-HOC accurately predicts indoor human occupancy for a large room using a model trained from a small room and adapted to the larger room. We evaluate DA-HOC with two baseline methods - support vector regression technique and SDHOC model. The results demonstrate that DA-HOC's performance is better by 12.29% in comparison to SVR and 10.14% in comparison to SD-HOC.
Subjects Ubiquitous Computing
Decision Support and Group Support Systems
Keyword(s) Transfer learning
domain adaptation
ambient sensing
building occupancy
presence detection
number estimation
cross-space modeling
contextual information
Copyright notice © 2017 by the Association for Computing Machinery, Inc. (ACM)
ISBN 9781450354769
Versions
Version Filter Type
Access Statistics: 51 Abstract Views  -  Detailed Statistics
Created: Wed, 19 Sep 2018, 13:35:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us