Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems are a major component of worldwide energy consumption, and frequently consist of complex networks of interconnected components. The ubiquitous nature of these systems suggests that improvements in their energy efficiency characteristics can have significant impact on global energy consumption. The complexity of the systems, however, means that decentralized control schemes will not always suffice to balance competing goals of energy efficiency and occupant comfort and safety. This dissertation proposes control solutions for three facets of this problem. The first is a cascaded control architecture for actuators, such as electronic expansion valves, that provides excellent disturbance rejection and setpoint tracking characteristics, as well as partial nonlinearity compensation without a compensation model. The second solution is a hierarchical control architecture for multiple-evaporator vapor compression systems that uses model predictive control (MPC) at both the supervisory and component levels. The controllers leverage the characteristics of MPC to balance energy efficiency with occupant comfort. Since the local controllers are decentralized, the architecture retains a degree of modularity—changing one component does not require changing all controllers. The final contribution is a new distributed optimization algorithm that is rooted in distributed MPC and is especially motivated by HVAC&R systems. This algorithm allows local level optimizers to iterate to a centralized solution. The optimizers have no knowledge of any plant other than the plant they are associated with, and only need to communicate with their immediate neighbors. The efficacy of the algorithm is displayed with two sets of examples. One example is simulation based, wherein a building is modeled in the EnergyPlus software suite. The other is an experimental example. In this example, the algorithm is applied to a multiple evaporator vapor compression system. In both cases the design method is discussed, and the ability of the algorithm to reduce energy consumption when properly applied is demonstrated.

In the wake of rising energy costs, there is a critical need for sustainable energy management of commercial and residential buildings. Buildings consume approximately 40% of total energy consumed in the US, and current methods to reduce this level of consumption include energy monitoring, smart sensing, and advanced integrated building control. However, the building industry has been slow to replace current PID and rule-based control strategies with more advanced strategies such as model-based building control. This is largely due to the additional cost of accurately modeling the dynamics of the building and the general uncertainty that model-based controllers can be reliably used in real conditions. The first half of this thesis addresses the challenge of constructing accurate grey-box building models for control using model identification. Current identification methods poorly estimate building model parameters because of the complexity of the building model structure, and fail to do so quickly because these methods are not scalable for large buildings. Therefore, we introduce the notion of parameter identifiability to determine those parameters in the building model that may not be accurately estimated and we use this information to strategically improve the identifiability of the building model. Finally, we present a decentralized identification scheme to reduce the computational effort and time needed to identify large buildings. The second half of this thesis discusses the challenge of using uncertain building models to reliably control building temperature. Under real conditions, building models may not match the dynamics of the building, which directly causes increased building energy consumption and poor thermal comfort. To reduce the impact of model uncertainty on building control, we pose the model-based building control problem as a robust control problem using well-known H1 control methods. Furthermore, we introduce a tuning law to reduce the conservativeness of a robust building control strategy in the presence of high model uncertainty, both in a centralized and decentralized building control framework.

The thesis provides a basic concept of intelligent building and related fields where this technology is appropriate. The next section describes the systems used in this field, especially the so-called communication buses and protocols. For an overview of developments in this area there is also a list of major companies involved in this technology. As a practical example is then described by air temperature and air control facilities in the building A2 or A5 in a building located within the Faculty of Mechanical Engineering, Brno University of Technology. For both cases are also proposed possible treatment.

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