Building Metadata Normalization Using Generative AI

The escalating global emissions attributable to commercial buildings call for the integration of digital technologies to enhance energy efficiency and occupant comfort. Cyber-physical control systems (CPCS) can collect data from various sensors and actuators in the building to provide valuable insights to building owners. However, the unstructured metadata text fields in these systems pose a challenge in leveraging artificial intelligence and machine learning solutions for building management. This paper proposes a solution using pre-trained large language models for building metadata normalization that addresses inconsistencies in the point text metadata across buildings. The evaluation of this solution is performed using two publicly-available CPCS datasets, revealing its ability to structure the unstructured natural language metadata without fine tuning or training from scratch.