We propose several control algorithms and compare their performance and complexity through simulations; the control algorithms regulate the indoor climate of commercial buildings. The goal of these control algorithms is to use occupancy information to reduce energy use-over conventional control algorithms-while maintaining thermal comfort and indoor air quality. Three novel control algorithms are proposed, one that uses feedback from occupancy and temperature sensors, while the other two compute optimal control actions based on predictions of a dynamic model to reduce energy use. Both the optimal control based schemes use a model predictive control (MPC) methodology; the difference between the two is that one is allowed occupancy measurements while the other is allowed occupancy predictions. Simulation results show that each of the proposed controllers lead to significant amount of energy savings over a baseline conventional controller without sacrificing occupant health and comfort. Another key finding is that the feedback controller performs almost as well as the more complex MPC-based controllers. In light of the complexity of the MPC algorithms compared to the feedback control algorithm, we conclude that feedback control is the more suitable one for occupancy based zone-climate control. A related conclusion is that the difficulty of obtaining occupancy predictions does not commensurate with the resulting benefits; though these benefits are a strong function of ventilation standards. (C) 2013 Elsevier Ltd. All rights reserved.