A multi-level architecture to facilitate MPC implementation in commercial buildings: basic principles and case study José A. Candanedo 1 , Vahid R. Dehkordi 2 , and Phylroy Lopez 3 1 CanmetENERGY-Varennes, NRCan, Varennes, Québec 2 CanmetENERGY-Ottawa, NRCan, Ottawa, Ontario Abstract This paper presents a methodology aimed at facilitating the de- ployment of model-based predictive control (MPC) in buildings. MPC has shown promise as an effective way to reduce utility costs associat- ed with peak demand, and to better manage the interaction between “smart buildings” and the “smart grid”. However, steps are needed to streamline the implementation of MPC in buildings and thus encour- age its adoption in building operation. The proposed architecture in- tends to contribute to this goal by enabling a “compartmentalized”, distributed, hierarchical approach to building modelling and controls. The proposed multi-level methodology allows formulating control problems so that the planning time horizon fits the scale of the system. A model of a commercial building, including thermal energy storage devices at different control levels, is used to demonstrate the method- ology. Low-order resistive-capacitance models for the thermal spaces are obtained from a detailed model created in EnergyPlus. 1 Introduction The application of model-based predictive control (MPC) to the operation of buildings has received a great deal of attention in recent years (Ma et al., 2010, Nghiem and Pappas, 2011, Siroký et al., 2011, Candanedo and Athienitis, 2011, Kim and Braun, 2012, Corbin et al., 2013). MPC has come to be recognized as an effective technique for improving load man- agement in high-performance buildings, and as a promising approach to the incorporation of renewable energy sources. MPC and similar methods are expected to play a key role for the integration of smart buildings in the smart grid. Despite these promising prospects, the practical implementation of a formal MPC strategy in a building –understood as the application of a model-based optimization algo- rithm– is a rather daunting task today. Reaching the state in which online MPC strategies can be applied in buildings in a timely and cost-effective manner requires advances in several are- as. These areas include appropriate modelling (Prívara et al., 2012, Eisenhower et al., 2012, Candanedo et al., 2013a); automatic formulation and solution of optimization problems (Cigler et al., 2013); tools for obtaining weather forecast information (Candanedo et al., 2013c); data-collection and modelling of occupancy (Oldewurtel et al., 2012, Gunay et al., 2013), among others. This paper presents the outline of an approach aimed at facilitating the testing of pre- dictive control strategies and the implementation of MPC in buildings (Candanedo and Dehkordi, 2013). This methodology is one of core components of a four-year project at our institution 1 ; it is based on the “dissection” of the complex structure of a commercial building into smaller control areas arranged hierarchically (e.g., room/thermal zone, group of zones, whole building), which may be nested into each other. With this method, relatively simple 1 Multi-level Control for Buildings (MLCB), EcoEII program.