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A self-contained model-based predictive control framework for HVAC systems using sequential proactive system identification
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DOI：10.1016/j.enbuild.2026.117587.png)
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Abstract
En 中文
• Proactive system identification enables automated inverse model training. • Disturbances representing solar and casual heat gains are estimated as time-variant parameters. • SARIMA models forecast disturbances for MPC. • MPC shifts HVAC loads away from on-peak electricity periods. • Heating and cooling costs reduced by 12% and 15%, respectively.
Keywords:
Model-based predictive control
Data-driven models
System identification
Disturbance forecasting
Automated demand response
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