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Model predictive control and fault detection and diagnostics of a building heating, ventilation, and air conditioning system.

Number: pap. 3616

Author(s) : BENGEA S., LI P., SARKAR S., et al.

Summary

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.

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Pages: 15 p.

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Details

  • Original title: Model predictive control and fault detection and diagnostics of a building heating, ventilation, and air conditioning system.
  • Record ID : 30013642
  • Languages: English
  • Source: 2014 Purdue Conferences. 3rd International High Performance Buildings Conference at Purdue.
  • Publication date: 2014/07/14

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