• DOI:10.1145/3217214
• Corpus ID: 54066723

A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data

@article{Ang2018ASR,
  title={A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data},
  author={Irvan Bastian Arief Ang and Margaret Hamilton and Flora D. Salim},
  journal={ACM Transactions on Sensor Networks (TOSN)},
  year={2018},
  volume={14},
  pages={1 - 28}
}

• I. A. Ang, M. Hamilton, Flora D. Salim
• Published 27 November 2018
• Psychology
• ACM Transactions on Sensor Networks (TOSN)

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 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 occupants with minimal training data: as little as 1 day’s 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 other rooms. We evaluate DA-HOC++ with two baseline methods: a support vector regression technique and an 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.