Building Energy Management Systems are finding widespread use for the holistic control of all energy-influencing elements of buildings and are responsible for ensuring an effective and parsimonious energy use. In most cases, fixed logic controllers are deployed in the building to implement predetermined strategies. Good performance can not be guar anteed due to inherent uncertainties that can not be a priori ascertained, such as weather variations, occupant actions, and changes in the building state and characteristics. In this paper, a model-assisted tuning methodology is presented to adaptively and automatically fine-tune relevant controller parameters. In our approach, at the end of each day of the building operation, given "reasonable" predictions for the following day, and using an accurate thermal-simulation model to evaluate performance, a new set of controller parameters is generated to be used the following day. This way, good performance can be achieved using controllers with simple mathematical structure.