Summary
Used machine learning to forecast facility and equipment-level energy demand in steel production. Enabled operators to anticipate peak usage and adjust production schedules accordingly. Reduced energy costs and exposure to price volatility while improving operational efficiency. Supported scalable deployment across multi-site manufacturing environments.