What question did this study set out to answer?

To advance building decarbonization by integrating leak detection with machine learning.

April 30, 2026Buildings0 citationsOpen Access

Unified AI Framework for Decarbonization in Large-Scale Building Energy Systems: Integrating Acoustic-Vision Leak Detection and Schedule-Aware Machine Learning

Key Points

To advance building decarbonization by integrating leak detection with machine learning.
Develop a dual-action AI framework combining acoustic-vision leak detection and schedule-aware machine learning.
Employ a probabilistic multimodal fusion algorithm with an ultrasonic camera for leak quantification.
Utilize an XGBoost model to predict dynamic power based on external conditions and operational schedules.
Achieved over 20% to 40% energy reduction compared to baseline operations.
Estimated annual reduction of 116 tons of CO2 emissions.

Abstract

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R2 = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO2 emissions, thereby providing a direct pathway toward carbon-neutral building operations.

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