Department of Computer Science & Engineering

HVAC systems constitute 40% of energy in commercial buildings, and faults in HVAC account for 5% to 20% of its energy consumption. Typically, HVAC in modern buildings are managed using Building Managing Systems (BMS), and fault detection is one of the essential services provided by BMS to keep HVAC operational. We study the fault management practices followed in real commercial buildings, and find that current techniques used for fault detection fail to detect large number of efficiency related faults. Based on our findings, we developed BuildingSherlock (BDSherlock), which is a web service based fault management framework that exposes building information to automatically detect faults using useful algorithms. We deploy BDSherlock in a 145000 sqft building at UC San Diego, successfully detected 87 faults using data driven analysis.

Pre-2018 CSE ID: CS2014-1007