The 40% problem
데이터 centers spend up to 40% of total energy on cooling. Most of that energy follows static rules — fixed setpoints, time-based schedules, and conservative safety margins. FactVerse AI Agent replaces static rules with dynamic optimization, continuously tuning cooling parameters based on actual thermal conditions and IT load.
From PUE dashboards to PUE optimization
Dashboards show you what your PUE is. AI Agent shows you how to improve it — identifying specific opportunities, simulating the expected impact, and recommending parameter changes with confidence-scored predictions.
Capacity planning that looks forward
Traditional capacity planning uses spreadsheets and rules of thumb. System Dynamics simulation models the complex interactions between IT load growth, cooling capacity, power density, and infrastructure constraints — predicting limits months before they become problems.
Why AI Agent for 데이터 Centers?
| Traditional DCIM | FactVerse AI Agent |
|---|---|
| Static cooling setpoints | Bayesian-optimized dynamic tuning |
| PUE monitoring dashboards | PUE optimization with actionable recommendations |
| Spreadsheet capacity planning | System Dynamics simulation for growth modeling |
| Manual compliance audits | Automated Green Mark/LEED/ISO 50001 reporting |
| Threshold-based alerting | AI anomaly detection for cascading failure prevention |
Related
- FactVerse AI Agent — Full platform overview
- 데이터 Centers — Industry overview
- Energy Optimization — Energy solution
- Smart Facility Management — Facility management
자주 묻는 질문
DFS는 OPC UA, BACnet, REST API 등 표준 프로토콜로 기존 시스템에 연결됩니다.