Experimental analysis of model-based predictive optimal control for active and passive building thermal storage inventory

This paper demonstrates model-based predictive optimal control of active and passive building thermal storage inventory in a test facility in real time using time-of-use differentiated electricity prices without demand charges. A novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. All primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real time while observing comfort and operational constraints. It was determined that even when the optimal controller is given imperfect weather forecasts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility cost savings relative to conventional building operation can be substantial. Central requirements are a facility that lends itself to passive storage utilization and a building model that includes a realistic plant representation. Savings associated with passive building thermal storage inventory proved to be small in this case because the test facility is not an ideal candidate for the investigated control technology. Moreover, the fitcility's central plant revealed the idiosyncratic behavior that the chiller operation in the ice-making mode was more energy efficient than in the chilled-water mode. Field experimentation is now required in a suitable commercial building with sufficient thermal mass, an active TES system, and a climate conducive to passive storage utilization over a longer testing period to support the laboratory findings presented in this study.