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

Chuanjie Cheng, Ruimin Nie (), Jing Pan (), Jia Zhu and Daguang Han
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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

Chapter Chapter 117 in Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate, 2024, pp 1697-1708 from Springer

Abstract: 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.

Keywords: Restricted Boltzmann Machine (RBM); Medical buildings; Carbon emission; Occupants activities (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-97-1949-5_118

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DOI: 10.1007/978-981-97-1949-5_118

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