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

Key

3616

Conference Year

2014

Authors

Veronica Adetola, United Technologies Research Center, United States of AmericaFollow
Sorin Bengea, United Technologies Research Center, United States of AmericaFollow
Keunmo Kang, United Technologies Research Center, United States of AmericaFollow
Anthony Kelman, University of California, BerkeleyFollow
Francesco Leonardi, United Technologies Research Center, United States of AmericaFollow
Pengfei Li, United Technologies Research Center, United States of AmericaFollow
Teems Lovett, United Technologies Research Center, United States of AmericaFollow
Soumik Sarkar, United Technologies Research Center, United States of AmericaFollow
Sergey Vichik, University of California, BerkeleyFollow

Keywords

Optimal control, Diagnostics, large-size HVAC

Abstract

The paper presents Model Predictive Control (MPC) and Fault Detection and Diagnostics (FDD) technologies, their on-line implementation, and results from several demonstrations conducted for a large-size HVAC system. 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 for the HVAC actuator loops which minimize energy consumption while meeting equipment operational constraints and occupant comfort constraints. The MPC algorithm is implemented using a new tool, the Berkeley Library for Optimization Modeling (BLOM), which generates automatically an efficient optimization formulation directly from a simulation model. The FDD algorithm detects and classifies in real-time potential faults of the HVAC actuators based on data from multiple sensors. The performance and limitations of FDD and MPC algorithms are illustrated and discussed based on measurement data recorded from multiple tests.