A Scalable Room Occupancy Prediction with Transferable Time SeriesDecomposition of CO\(_\mbox2\) Sensor Data

Publication Year: 2018 Publication Type : JournalArticle


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.


    author = {Irvan Bastian Arief Ang and Margaret Hamilton and Flora D. Salim},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/journals/tosn/AngHS18.bib},
    doi = {10.1145/3217214},
    journal = {ACM Trans. Sens. Networks},
    number = {3-4},
    pages = {21:1--21:28},
    timestamp = {Tue, 12 May 2020 01:00:00 +0200},
    title = {A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO\(_\mbox2\) Sensor Data},
    url = {https://doi.org/10.1145/3217214},
    volume = {14},
    year = {2018}


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