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{arief2017hoc,
title={DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data},
author={Arief-Ang, Irvan B and Salim, Flora D and Hamilton, Margaret},
booktitle={Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments},
pages={1--10},
year={2017}
}
© 2021 Flora Salim - CRUISE Research Group.