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

  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)},
  pages={1 - 28}
Human occupancy counting is crucial for both space utilisation and building energy optimisation. [] Key Method 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…

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  • Irfanullah KhanA. GuerrieriG. SpezzanoA. Vinci
  • Engineering
    2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
  • 2022
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