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 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 five different rooms across different countries using a model trained from a small room and adapted to the other rooms. We evaluate DA-HOC++ with two baseline methods - support vector regression technique and 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.
@article{DBLP:journals/tosn/AngHS18,
author = {Irvan Bastian Arief Ang and
Margaret Hamilton and
Flora D. Salim},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/journals/tosn/AngHS18.bib},
doi = {10.1145/3217214},
journal = {ACM Trans. Sens. Networks},
number = {3-4},
pages = {21:1--21:28},
timestamp = {Tue, 12 May 2020 01:00:00 +0200},
title = {A Scalable Room Occupancy Prediction with Transferable Time Series
Decomposition of CO\(_\mbox2\) Sensor Data},
url = {https://doi.org/10.1145/3217214},
volume = {14},
year = {2018}
}
© 2021 Flora Salim - CRUISE Research Group.