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{arief2018scalable,
title={A scalable room occupancy prediction with transferable time series decomposition of CO2 sensor data},
author={Arief-Ang, Irvan B and Hamilton, Margaret and Salim, Flora D},
journal={ACM Transactions on Sensor Networks (TOSN)},
volume={14},
number={3-4},
pages={1--28},
year={2018},
publisher={ACM New York, NY, USA}
}
© 2021 Flora Salim - CRUISE Research Group.