Data-Enabled Predictive Temperature and Humidity Control in a Historical Museum Building

The following paper examines Data-Enabled Predictive Control (DeePC) for combined temperature and humidity control in a historical museum building. Bypassing classical offline system identification techniques, DeePC uses a nonparametric model only based on previously collected closed loop input-output data. Within a co-simulation consisting of MATLAB and EnergyPlus, the control performance while ensuring the preventive conservation requirements is evaluated. The impact of both the hyperparameters and measurement noise is investigated. The results show that DeePC maintains temperature and humidity within the limits for cultural asset protection. Integrating disturbances and weather forecasts into DeePC to enhance control performance and energy efficiency could be considered in future work.