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Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering

Author

Listed:
  • Hua Chen

    (School of Economics, Fujian Normal University, Fuzhou 350117, China
    These authors contributed equally to this work.)

  • Shuang Dai

    (Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, UK
    These authors contributed equally to this work.)

  • Fanlin Meng

    (Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, UK)

Abstract

A customized and cost-effective building thermal control system is critical for accommodating thermal performance differences within the building, as well as satisfying the individual thermal comfort needs of occupants. Moreover, incorporating a building indoor thermal simulation procedure into the thermal control system can reduce the necessity of installing various expensive sensors (e.g., wearable sensors for personal thermal comfort management) in individual offices, as well as the requirement of extensive computing facilities without rendering the control performance, resulting into more sustainable building operations. An important step in achieving the above-mentioned goal is understanding how different offices/rooms behave differently given the same outdoor weather conditions. This study proposes a smart building indoor thermal profiling system to identify underlying physical factors that affect thermal performance in different seasons and to track dynamic cluster trajectories of considered offices to suggest indoor thermal optimization strategies. A consensus-based clustering approach is adopted to robustly cluster offices into different groups based on their hourly indoor temperature profiles for different seasons. Experimental results showed that our proposed approach could effectively discover more indoor thermal patterns in the buildings and is able to identify distinct dynamic cluster trajectories across four seasons (i.e., eight distinct dynamic trajectories in our case study). The data-driven analysis conducted in this study also indicated promising applications of the proposed smart building indoor thermal profiling system in effectively guiding the design of customized thermal control strategies for buildings. It also suggested that the proposed approach could be applied to a wide range of other applications, such as customized building energy management, energy pricing, as well as the economic benefit analysis of building retrofits and design.

Suggested Citation

  • Hua Chen & Shuang Dai & Fanlin Meng, 2023. "Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15489-:d:1271723
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    References listed on IDEAS

    as
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