Case Study
Apex Energy
Smart grid optimization using real-time demand forecasting and asset allocation.
Energy & Utilities
19% load optimization
Outcome
$28.3M energy cost reduction
Quantified Impact
Challenge
Manual load balancing across generation assets incurred peak surcharges.
Strategy
Built a real-time optimization engine predicting demand 4-6 hours ahead.
Execution
- • Ingested weather, calendar, and historical demand signals.
- • Trained ensemble models for demand forecasting by region.
- • Integrated with dispatch systems for automated asset allocation.
Tech stack
XGBoostProphetSparkKubernetesGrafana
Results
- • 19% reduction in peak-hour load
- • 12% overall energy cost savings
- • 99.97% uptime for optimization service
Testimonial
"The system paid for itself in the first optimization quarter."
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