Abstract
In this work, the potential energy savings from adapting to daily ambient temperature differences for frozen cargo in reefer containers are studied using a model of the Star Cool reefer. The objective is to create a controller that can be implemented on an embedded system, and a range of methods are used to reduce the computational load. A combination of MPC and traditional control is used, and the accuracy of the MPC is enhanced with an on-line update of the model parameters. The simulation experiments show that potential energy savings of up to 21% are achieved when the MPC is allowed to control both the cooling capacity and the ventilation of the cargo area. The largest cost reduction is achieved through a reduced ventilation rate.
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