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Building Automation System Metadata Inference

Consistent metadata describing sensors and actuators in buildings is critical to deploy fault detection and diagnosis (FDD) applications on multiple buildings, which can enhance building performance, reduce energy consumption and improve occupant comfort. This project targets the development of a metadata inference framework to provide operational information, i.e., the metadata associated with sensors and actuators, for FDD applications applied to secondary heating, ventilation, and air conditioning (HVAC) systems.

Mario Bergés
Professor of Civil and Environmental Engineering

My research interests vary, but generally gravitate towards the development of technologies to make our built enviornment and the communities in them more autonomous and efficient.