12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China

Research Article

A Time-aware Method for Occupancy Detection in a Building

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  • @INPROCEEDINGS{10.4108/eai.29-6-2019.2282388,
        author={Ling  Song and Xiaofei  Niu and Qiang  Lyu and Shunming  Lyu and Tian  Tian},
        title={A Time-aware Method for Occupancy Detection in a Building},
        proceedings={12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2019},
        month={6},
        keywords={occupancy detection; building consumption; time-aware method},
        doi={10.4108/eai.29-6-2019.2282388}
    }
    
  • Ling Song
    Xiaofei Niu
    Qiang Lyu
    Shunming Lyu
    Tian Tian
    Year: 2019
    A Time-aware Method for Occupancy Detection in a Building
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.29-6-2019.2282388
Ling Song1, Xiaofei Niu1,*, Qiang Lyu2, Shunming Lyu3, Tian Tian1
  • 1: School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
  • 2: State Grid of China Technology College,Jinan 250002, China
  • 3: School of Information Technology & Electrical Engineering, University of Queensland, Queensland, Australia
*Contact email: niuxiaofei2002@163.com

Abstract

The target of buildings’ energy efficient is to facilitate a comfortable environment for occupants while maintaining minimal energy consumption. Occupant behaviors pay a large impact in influencing the energy consumption. Time-aware occupancy detection could give information support for intelligent building energy management. In this paper several building occupancy detection methods, which are based on the temporal analysis of historical data, are proposed for providing different size of prediction window occupancy detection. Each proposed approaches are evaluated against accurate real-life data collected from a building. Experiments have been conducted using actual occupancy data under six different time horizons can be used to perform time-aware occupancy states prediction. The experimental results show that Stochastic Gradient Descent (SGD) and Gaussian mixture models-Hidden Markov Model (GMM-HMM) outperforms the other methods for the evaluation. With proposed more accurate time-aware occupancy prediction algorithms, we hope to develop more energy-efficient HVAC(Heating, Ventilation, and Air Conditioning) scheduling systems in order to save energy consumption.