A data-driven optimization approach for minimization of the cooling output of an air handling unit (AHU) is presented. The models used in this research are built with data mining algorithms. The performance of dynamic models build by four different data mining algorithms is studied. A model extracted by a neural network is selected for identifying the functional mapping between specific outputs and controllable and non-control table inputs of the AHU. To minimize the cooling output while maintaining the corresponding thermal properties of the supply air within a certain range, a bi-objective optimization model is proposed. The evolutionary strategy algorithm is applied to solve the optimization problem with the optimal control settings obtained at each time stamp. The minimized AHU's cooling output reduces the chiller's load, which leads to energy savings. (C) 2009 Elsevier Ltd. All rights reserved.