Data Driven Investigation of Faults in HVAC Systems with Model, Cluster and Compare (MCC)

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Provided by: Association for Computing Machinery
Topic: Data Management
Format: PDF
The complexity of modern HVAC systems leads to device misconfiguration in about 40% of buildings, wasting upto 40% of the energy consumed. Fault detection methods generate excessive alarms leading to operator alert fatigue, faults left unfixed and energy wastage. Sophisticated fault detection techniques developed in the literature are seldom used in practice. The authors investigate this gap by applying various fault detection techniques on real data from a 145,000 sqft, five floor building. They first find that none of these algorithms are designed to capture control loop configuration faults.
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