In this talk we consider the modeling and control of building systems represented as large and complex systems. First, we present a motivation for why we believe a centralized approach to optimal operation may not be the best approach. Then, we present an approach for clustering building zones so as to create a decentralized architecture that balances achievable performance with tolerance to sensor/actuator faults. Here, performance is measured by temperature regulation. The clustering procedure is an agglomerative one in which we define 2 metrics that result in a Pareto-optimal tradeoff and then search for a Nash-like equilibrium between them. Subsequently, we design a model predictive control (MPC) approach for performing on-line optimization of controlled HVAC within a building. An Energy-Plus simulation of a commercial building under MPC control is given to demonstrate the approach. Time permitting, we will discuss the HVAC mechanical systems within buildings. The discussion will start from a complex systems-level viewpoint and drill down to physical modeling of multi-phase flows in heat-pump/AC systems.