A Distributed Approach to Efficient Model Predictive Control of Building HVAC Systems

Model based predictive control (MPC) is increasingl y being seen as an attractive approach in controlli ng building HVAC systems. One advantage of the MPC approach is the ability to integrate weather forecast, occupanc y information and utility price variations in determi ning the optimal HVAC operation. However, application to largescale building HVAC systems is limited by the large number of controllable variables to be optimized a t every time instance. This paper explores techniques to reduce the computational complexity arising in applying MPC to the control of large-scale buildings. We formulate the task of optimal control as a distributed optimizati on problem within the MPC framework. A distributed optimization approach alleviates computational costs by simult aneously solving reduced dimensional optimization problems at the subsystem level and integrating the resulting solutions to obtain a global control law. Additional computation al efficiency can be achieved by utilizing the occu pancy and utility price profiles to restrict the control laws to a piecewise constant function. Alternatively, u nder certain assumptions, the optimal control laws can be found analytically using a dynamic programming based approach without resorting to numerical optimization routine s leading to massive computational savings. Initial results of simulations on case studies are presented to compar e the proposed algorithms.