Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System

The paper presents the development and application of Model Predictive Control (MPC) and Fault Detection and Diagnostics (FDD) technologies to a large-scale HVAC system, their on-line implementation, and results from several demonstrations. The two technologies are executed at the supervisory level in a hierarchical control architecture as extensions of a baseline Building Management System (BMS). The MPC algorithm generates optimal set points, which minimize energy consumption, for the HVAC actuator loops while meeting equipment operational constraints and occupant thermal-comfort constraints. The MPC algorithm is implemented using a new computational toolbox, the Berkeley Library for Optimization Modeling (BLOM), which generates automatically an efficient optimization formulation directly from a simulation model. The FDD algorithm uses heterogeneous sensor data to detect and classify in real-time potential faults of the HVAC actuators. The performance and limitations of FDD and MPC algorithms are illustrated and discussed based on measurement data recorded from multiple tests.