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DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data

Published: 08 November 2017 Publication History

Abstract

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 SD-HOC 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.

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    cover image ACM Conferences
    BuildSys '17: Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments
    November 2017
    292 pages
    ISBN:9781450355445
    DOI:10.1145/3137133
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 08 November 2017

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    Author Tags

    1. ambient sensing
    2. building occupancy
    3. contextual information
    4. cross-space modeling
    5. domain adaptation
    6. number estimation
    7. presence detection
    8. transfer learning

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    • (2024)Inter-seasons and Inter-households Domain Adaptation Based on DANNs and Pseudo Labeling for Non-Intrusive Occupancy Detection非侵入型在宅推定に対するDANNsと疑似ラベリングをもとにした季節間および世帯間の教師なしドメイン適応手法Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.39-5_E-O4139:5(E-O41_1-13)Online publication date: 1-Sep-2024
    • (2024)Adapting Wireless Network Configuration From Simulation to Reality via Deep Learning-Based Domain AdaptationIEEE/ACM Transactions on Networking10.1109/TNET.2023.333534632:3(1983-1998)Online publication date: Jun-2024
    • (2024)From What You See to What We Smell: Linking Human Emotions to Bio-Markers in BreathIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327521615:2(465-477)Online publication date: Apr-2024
    • (2024)Multi-Source Domain Adaptation Using Ambient Sensor DataApplied Artificial Intelligence10.1080/08839514.2024.242932138:1Online publication date: 19-Nov-2024
    • (2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
    • (2023)Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy DetectionProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623718(1-10)Online publication date: 15-Nov-2023
    • (2023)Refining Nonparametric Mixture Models with Explainability for Smart Building Applications2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394196(5212-5217)Online publication date: 1-Oct-2023
    • (2023)Semisupervised Learning-Based Occupancy Estimation for Real-Time Energy Management Using Ambient DataIEEE Internet of Things Journal10.1109/JIOT.2023.328036110:20(18426-18437)Online publication date: 15-Oct-2023
    • (2023)Unsupervised domain adaptation without source data for estimating occupancy and recognizing activities in smart buildingsEnergy and Buildings10.1016/j.enbuild.2023.113808(113808)Online publication date: Dec-2023
    • (2023)Unsupervised domain adaptation with and without access to source data for estimating occupancy and recognizing activities in smart buildingsBuilding and Environment10.1016/j.buildenv.2023.110651243(110651)Online publication date: Sep-2023
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