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VACUUM ›› 2026, Vol. 63 ›› Issue (3): 97-103.doi: 10.13385/j.cnki.vacuum.2026.03.14

• Vacuum Technology Application • Previous Articles     Next Articles

In-situ Monitoring and Attenuation Prediction Model for Vacuum Degree of Vacuum Insulation Panel and Its Application in Building Energy Saving Regulation

TANG Tong, ZONG Feichao, YANG Wei, GAO Xueyang   

  1. Northwest Institute of Nuclear Technology, Xi'an, 710600, China
  • Received:2025-08-14 Online:2026-05-25 Published:2026-06-01

Abstract: Aiming at the problem of insulation performance degradation caused by vacuum maintenance failure of vacuum insulation panels (VIP) in the field of building energy conservation under long-term service, this paper investigated in-situ monitoring of vacuum maintenance status for VIP and dynamic control of building energy conservation. Built a multimodal vacuum status monitoring platform to real-time collect key parameters of VIP core material vacuum maintenance status in situ; combining the improved particle filter algorithm, a vacuum attenuation dynamic model was constructed to dynamically predict the degradation trajectory of VIP's vacuum maintenance ability under different harsh climate conditions; developed a dynamic optimization system for building envelope structures based on deep reinforcement learning, coupled with BIM models and real-time vacuum monitoring data, to adaptively adjust the operating parameters of the building energy management system.The result shows that the research realizes the in-situ, continuous and accurate monitoring of the key parameters of VIP vacuum maintenance state in service. The vacuum decay prediction model provides a new method for evaluating the long-term service reliability of VIP. The optimization system based on real-time perception of vacuum state can effectively compensate the adiabatic loss caused by vacuum attenuation and significantly reduce building energy consumption.

Key words: vacuum insulation board, vacuum monitoring, gas permeability, in situ monitoring, particle swarm optimization

CLC Number:  TB79

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