AI for energy forecasting and control.
Improve energy efficiency, forecast power demand or power usage, and get actionable insights from AI.
Where is energy used and what are the root causes of energy losses?
Energy efficiency is about cutting costs, yes. But it’s also about understanding where energy is lost.
Although most plants know how much energy they consume, they typically do not know where losses originate or which operational conditions cause them.
Hybrid AI models
Hybrid AI models leverage first principles (thermodynamics, fluid dynamics, electrical models), high-frequency sensor data, and historical operating regimes to categorize total energy consumption:
- Useful energy
- Structural losses (design or aging)
- Operational losses (setpoints, sequencing, transient behavior)
AI’s actionable outputs
- Energy loss attribution by asset, process phase, and operating condition
- Sensitivity analysis: “If this variable changes, energy impact is X”
- Recommended optimal operating envelopes with confidence bounds
AI’s real business value
- Identifies actionable inefficiencies, not just anomalies without root causes
- Enables targeted retrofits or dynamic set-point control changes
- Builds trust by embedding physical laws into AI predictions
Can you predict energy demand?
Forecast energy demand with tailor-made AI-based forecasting models that:
- Predict energy demand at process, line, or asset level
- Are conditioned on production plans, recipes, weather, and equipment states
- Adapt to regime changes (startups, shutdowns, grade changes)
AI’s actionable outputs
- Short-term (minutes–hours) and mid-term (days–weeks) demand forecasts
- Scenario-based forecasts tied to production schedules
- Probabilistic forecasts to support risk-aware planning
AI’s real business value
- Improves contract negotiation and peak demand management
- Reduces penalties from demand spikes
- Enables proactive coordination with utilities or on-site generation
Do you know which energy to use, when, and how much?
Turn to AI-driven energy dispatch, curtailment and optimization for guidance.
Industrial sites increasingly face:
- Variable energy prices
- Demand response programs
- On-site renewables and storage
- Grid constraints
AI application
The optimization and control AI:
- Arbitrates between grid power, on-site generation, storage, and flexible loads
- Considers process constraints, ramp rates, and product quality
- Uses forecasts (prices, load, renewables) to make forward-looking decisions
AI’s actionable outputs
- Optimal energy sourcing and consumption schedules
- Automated curtailment strategies with quantified production impact
- Decision support for demand response participation
AI’s real business value
- Turns energy flexibility into a financial asset
- Avoids “blind” curtailment that disrupts production
- Supports decarbonization without sacrificing throughput
