A Quantitative Model-Based Fault Detection & Diagnostics (FDD) System for Zone-Level Inefficiencies

Heating, ventilation and air-conditioning (HVAC) systems account for a significant portion of energy consumption in buildings. The majority of fault detection research has neglected zone-level faults. In this study, the methodology of a quantitative model-based fault detection and diagnostics (FDD) system for the zone-level is presented. The creation of a basic model was completed using Matlab. Analyses were conducted, identifying five zone-level inefficiencies. The severity of these inefficiencies was analyzed and an excessive amount of air handling unit (AHU) fan energy consumption was detected. The redundancy of these faults in the building force the AHU to expend an unwarranted amount of energy, highlighting the importance of utilizing FDD. Benefits of this methodology include the detection of pre-existing faults originating from initial design, as well as the ability to apply this to new or existing buildings when sufficient sensing and metering infrastructure is available, improving the efficiency of the building.