AI-Powered Digital Twin for Sustainable Campus Facility Management

University campus buildings consume significant energy through chilled water (CHW) systems, yet facility and energy managers lack tools that connect sensor data to actionable, spatially grounded decisions without requiring programming expertise. We present BearcatAI, an end-to-end analytics platform developed at the University of Cincinnati (UC) that unifies machine learning demand prediction, a large language model (LLM) conversational interface, and an interactive 3D campus visualization derived from geographic information system (GIS) data. All models were trained and evaluated on 271 days of daily meter data collected across the UC building portfolio (June 2025–February 2026); ten years of monthly CHW records (July 2015–July 2025) provided long-term context only. An ensemble of XGBoost and Random Forest models achieves an in-sample R2=0.988, mean absolute percentage error (MAPE) =20.4% (inflated by near-zero winter readings), and normalized mean bias error =−0.3% on DAAP (College of Design, Architecture, Art, and Planning), the priority building identified by the UC utility engineering team. Beyond building-specific models, we introduce use-type models that pool buildings by functional category (Academic, Research, Administrative, Housing, Recreation, Medical) without any building-specific identity features. Leave-One-Building-Out (LOBO) cross-validation shows that the Research type model generalizes to unseen research buildings with median R2=0.911, and the Academic model achieves R2=0.918 on DAAP without having seen it during training, whereas medical and hospital types do not generalize, demonstrating the feasibility of functional type models as campus-scale benchmarking tools within the study dataset. A natural language interface powered by BearcatGPT, UC’s hosted instance of GPT-4.1, allows utility managers to query anomalies, request forecasts, and generate building charts through conversation. Anomalous buildings are automatically highlighted in a 3D campus model spanning UC Main Campus, Medical Campus, and Digital Futures. The results are limited to a single campus and observation period; user evaluation and a robotic inspection module under development remain future work.