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UID:pretalx-amfc2026-F8Z33G@modelica.simtek.cc
DTSTART;TZID=CST:20260921T120500
DTEND;TZID=CST:20260921T123000
DESCRIPTION:Large-scale HVAC systems in public buildings account for approx
 imately 40% of total building energy consumption. This presentation report
 s an industrial deployment of an FMI-based Model Predictive Control (MPC) 
 framework with AI-driven adaptive optimization for a university library's 
 central air-conditioning system. A Modelica-based digital twin of the HVAC
  system was developed using FMI co-simulation architecture (Modelica Assoc
 iation 2026a\; b). The digital twin is continuously updated with real-time
  IoT sensor data\, enabling the MPC controller to minimize energy consumpt
 ion while maintaining thermal comfort. An AI-driven iterative strategy aut
 omatically adjusts model parameters and control policies based on operatio
 nal feedback (Zhang et al. 2026). \nField deployment through integration w
 ith the existing Building Automation System (BAS) achieved 15%–20% energ
 y reduction versus conventional control\, with 10-minute optimization cycl
 es. The talk will share practical deployment experience\, lessons learned 
 from laboratory prototyping to industrial implementation\, and open challe
 nges in scaling AI-enhanced MPC for building energy management.
DTSTAMP:20261004T070733Z
LOCATION:FMI & MBSE (R2001)
SUMMARY:An AI-Enhanced FMI-Based Model Predictive Control Framework with Io
 T-Driven Digital Twin Updating for Energy-Efficient HVAC Systems(IUP) - Xu
 elian Lei
URL:https://modelica.simtek.cc/amfc2026/talk/F8Z33G/
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