A DATA-DRIVEN SEMANTIC-RICH DIGITAL TWIN SYSTEM FOR MONITORING AND ANALYZING ACMV PERFORMANCE IN TROPICAL ENVIRONMENTS
CHEN XIAYI
CHEN XIAYI
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Abstract
The escalating global energy crisis necessitates a resolute commitment to achieving net-zero emissions by 2050. With buildings accounting for 39% of energy consumption, particularly in ACMV operations, their significant impact cannot be overstated. In tropical regions like Singapore, where ACMV systems consume up to 60% of total energy for occupant comfort, sustainable solutions are imperative. DT emerges as realistic replicas offering insights into spatial physics, systems’ performance, and occupants’ comfort for FM. However, existing research on ACMV operations through DT remains fragmented, prompting the proposal of a comprehensive system for reconstructing semantic-rich DT to facilitate FM’s operation management. Therefore, the proposed approach harmonizes data preparation, modelling, analytics, and application presentation by integrating rule-based and machine learning-driven methodologies, along with analysis results, with a Scan-to-BIM model for comprehensive integrated monitoring. This integration facilitates the comprehensive monitoring and assessment of ACMV and indoor comfort parameters, demonstrated through a case study conducted in a research office. The research contributes to the development of DT for FM, offering valuable insights for intelligent decision-making, and promoting energy efficiency and indoor comfort in the context of tropical ACMV operations.
Keywords
Digital Twin; Machine Learning; Data-driven; Facilities Management;Air Conditioning & Mechanical Ventilation; Scan-to-BIM
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Date
2023-09-01
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