AI Data Center Energy Planning
The Problem
“Forecast data center power needs for real estate”
Organizations face these key challenges:
1
Uncertain tenant ramp schedules and power density needs cause chronic over/under-sizing of electrical and cooling systems.
2
Limited visibility into grid capacity, interconnection queue risk, and tariff volatility leads to delayed energization and unexpected operating costs.
3
Manual scenario planning cannot keep up with rapid changes in leasing pipeline, equipment efficiency, weather extremes, and sustainability requirements.
Impact When Solved
5–12% reduction in electrical/cooling overbuild through probabilistic load forecasting and phased capacity planning.2–6 months faster time-to-power by prioritizing interconnection actions and de-risking utility coordination with predictive queue analytics.5–10% lower annual energy cost via AI-optimized procurement, peak management, and storage/generation dispatch under reliability constraints.