01 — Data Generation & Setup
Generates a synthetic 8-week chiller plant dataset with three embedded operational faults for diagnostic training.
chiller-plant synthetic-data fault-injection
Pythonnumpypandasmatplotlib
Key Findings
- ● 1,344 hourly data points simulating a central chiller plant (June–July 2025)
- ● Three hidden faults injected: low-delta-T syndrome, cooling tower degradation, night-time fan bias
- ● 23 columns covering temperatures, flows, power, and derived KPIs
Synthetic chiller plant dataset creation with embedded operational faults for energy management diagnostic training. The dataset simulates real-world HVAC conditions including weather drivers, occupancy patterns, and cooling load dynamics.