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Measuring Occupants Activities-Generated Carbon Emissions in Healthcare Facilities Using Deep Learning

Author

Listed:
  • Chuanjie Cheng

    (China Design Digital Technology Co.)

  • Ruimin Nie

    (China Design Digital Technology Co.)

  • Jing Pan

    (China Design Digital Technology Co.)

  • Jia Zhu

    (China Design Digital Technology Co.)

  • Daguang Han

    (Southeastern University)

Abstract

This article proposes a method to measure occupants’ activities-generated carbon emissions in healthcare facilities using deep learning. The method employs a Restricted Boltzmann Machine (RBM) and a Deep Belief Network (DBN) within the SGAM framework to extract useful features from high-dimensional data and generate predictive and evaluation models. A motivating case in a hospital in Tianjin, China is used to demonstrate the necessity of measuring occupants’ activities, which involves the development of an information integration system with collection, monitoring, operation, and control functionalities. The platform collects data from IoT devices and O&M platforms to form a database, which is updated hourly. The evaluation model is used to determine whether the model needs to be updated. The proposed method provides a way to monitor building operations and control strategies to reduce carbon emissions.

Suggested Citation

  • Chuanjie Cheng & Ruimin Nie & Jing Pan & Jia Zhu & Daguang Han, 2024. "Measuring Occupants Activities-Generated Carbon Emissions in Healthcare Facilities Using Deep Learning," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-1949-5_118
    DOI: 10.1007/978-981-97-1949-5_118
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