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.
@inproceedings{DBLP:conf/sensys/AngSH17,
author = {Irvan Bastian Arief Ang and
Flora D. Salim and
Margaret Hamilton},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/sensys/AngSH17.bib},
booktitle = {Proceedings of the 4th ACM International Conference on Systems for
Energy-Efficient Built Environments, BuildSys 2017, Delft, The Netherlands,
November 08-09, 2017},
doi = {10.1145/3137133.3137146},
editor = {Kamin Whitehouse and
Prabal Dutta and
Hae Young Noh},
pages = {1:1--1:10},
publisher = {ACM},
timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
title = {DA-HOC: semi-supervised domain adaptation for room occupancy prediction
using CO\(_\mbox2\) sensor data},
url = {https://doi.org/10.1145/3137133.3137146},
year = {2017}
}
© 2021 Flora Salim - CRUISE Research Group.