DA-HOC: semi-supervised domain adaptation for room occupancy predictionusing CO\(_\mbox2\) sensor data

Publication Year: 2017 Publication Type : ConferenceProceeding


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.


    author = {Irvan Bastian Arief Ang and Flora D. Salim and Margaret Hamilton},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/conf/sensys/AngSH17.bib},
    booktitle = {Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2017, Delft, The Netherlands, November 08-09, 2017},
    doi = {10.1145/3137133.3137146},
    editor = {Kamin Whitehouse and Prabal Dutta and Hae Young Noh},
    pages = {1:1--1:10},
    publisher = {ACM},
    timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
    title = {DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO\(_\mbox2\) sensor data},
    url = {https://doi.org/10.1145/3137133.3137146},
    year = {2017}


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