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}
}
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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