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AI FOR ENERGY & UTILITIES

Custom AI/ML solutions for energy & utilities industry

Custom AI and ML for utilities, IPPs, and energy traders. Asset failure prediction, wildfire risk modeling, EV charging demand, and grid optimization — built for regulator-grade defensibility.
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25–40%

Outage reduction from predictive maintenance on transmission and distribution assets.

200–300%

ROI from non-critical-asset predictive maintenance with 6–9 month deployment.

8–15%

Margin improvement from AI-optimized energy trading and dispatch.

Achieve immediate, organization-wide results

Six measurable outcomes across underwriting, claims, and actuarial functions — deployed in months, not years.

Asset Failure Prediction

Transformer, turbine, and substation failure prediction from sensor + thermal + maintenance history. 25–40% fewer outages.

Wildfire Risk Modeling

Computer vision + LiDAR + weather to score ignition risk per circuit. National Grid and Western IOUs already running this in production.

EV Charging & Load Forecasting

Per-feeder load models that integrate EV adoption, heat-pump rollout, and demand-response enrollment.

Grid Optimization & Dispatch

Power flow, congestion, and DER orchestration with reinforcement learning. 8–15% margin uplift on energy trading.

Outage Prediction & Storm Response

Pre-storm circuit risk scoring fused with crew-dispatch optimization for faster restoration.

Regulatory & R&D Research AI

RAG-grounded research-AI over FERC orders, PUC filings, NERC standards, IRENA reports, and your internal regulatory + renewable-tech research library. CSRD / IFRS S2 emissions reporting included.

Capabilities across the energy & utilities value chain

Grid Reliability & Asset Health

  • Transformer, turbine, and substation failure prediction
  • Vegetation-encroachment vision from satellite and LiDAR
  • Battery and renewable-asset performance modeling
  • DER (BESS, solar, EV) coordination and aggregation

Wildfire & Risk Management

  • Ignition-risk scoring per circuit and pole
  • PSPS (Public Safety Power Shutoff) decision support
  • Drone imagery and thermal-anomaly detection
  • Storm-impact prediction and crew pre-positioning

Demand Forecasting & Trading

  • Per-feeder load forecasting with EV, heat-pump, and DR signals
  • Day-ahead and intraday energy-market trading models
  • Renewable-output forecasting (solar, wind) with ensemble methods
  • Congestion and locational-marginal-price prediction

Customer & Field Operations

  • Outage-call classification and crew routing
  • Meter anomaly and revenue-protection scoring
  • Customer-segment energy-program targeting (DR, EE, EV)
  • Worker-safety vision and PPE compliance monitoring
From the playbook

How a Western IOU cut wildfire-driven PSPS hours 38% with circuit-level risk scoring

A Western U.S. investor-owned utility was triggering 12-day PSPS events annually that affected 500,000+ customers and triggered regulator scrutiny. We built a circuit-level wildfire risk scoring model that fused weather forecasts (NOAA HRRR), vegetation health (multispectral satellite), historical ignition data, and live asset health from SCADA telemetry. PSPS-hour exposure dropped 38% within one fire season, and the regulator-facing risk-spend prioritization freed up $22M in capital that was being spent on lower-risk circuits. The same vegetation models now drive a 5-year trim-cycle plan that reduces vegetation-caused outages by 45%.

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Speak with an energy & utilities AI expert

A 45-minute scoping call. We’ll come prepared with your appetite, your loss-cost benchmarks, and a directional read on which models move the needle on your line of business.

Ask us about

  • Transformer and substation failure prediction
  • Wildfire ignition-risk scoring per circuit
  • EV and heat-pump load forecasting per feeder
  • DER and battery-storage orchestration
  • Energy-trading and renewable-output models
  • Regulatory and renewables research-AI (incl. CSRD / IFRS S2 emissions reporting)

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    Need experts to collaborate with for your AI/ML journey? Drop us an email and we will get in touch

    Frequently asked questions

    How do you handle regulator-grade defensibility for AI in rate cases?
    Every model ships with documentation a state PUC, FERC, or NERC reviewer expects: tiered model-risk classification, validation against holdout periods, explainability artifacts, and ongoing performance monitoring. We've shipped models that have passed prudency review in rate cases and audit by big-four accounting firms for SOX 404 controls.
    Yes. We ingest from Schneider EcoStruxure, GE PowerOn, ABB Network Manager, Siemens Spectrum, and standard CIM/IEC 61968 systems. Weather feeds include NOAA HRRR, RTMA, and proprietary mesoscale products (DTN, AWIS). Outputs publish back to ADMS, GIS (ESRI), and OMS in formats your dispatchers and vegetation managers already use.
    No. Models ship with full MLOps wrappers — feature stores, retraining schedules, drift monitoring, and dashboards. Your asset, control-room, and trading teams interact through familiar BI and SCADA interfaces, not Jupyter notebooks. Hand-off documentation supports both DIY operation and managed-service continuation.
    Yes. The underlying ML platform (sensor ingestion, feature store, model registry, monitoring) is reusable across the value chain. Domain models differ — generation focuses on plant optimization and emissions, T&D on asset health and DER, retail on customer load and DR — but most integrated utilities run one shared stack with multiple model families.

    Explore AI/ML solutions for energy & utilities

    Ready to talk energy & utilities AI?

    Start with a 45-minute strategy session. We come prepared with a directional read on your line of business and a scoped proposal.

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