AI in Energy & Infrastructure

Artificial Intelligence is becoming a critical enabler in the modernization, optimization, and resilience of both infrastructure and energy systems. From predictive maintenance of EVSE networks to real-time energy balancing across microgrids, AI is driving cost reduction, uptime improvement, and efficiency gains at every stage of deployment. AI also plays a pivotal role in integrating intermittent renewables, managing grid congestion, forecasting demand, and supporting autonomous decision-making in complex energy and infrastructure environments.

AI Applications by Domain

Charging Infrastructure / EVSE

Apps: Predictive maintenance, dynamic pricing, charger utilization forecasting, load balancing.
Benefits: Higher uptime, improved ROI, reduced grid impact.

Grid Modernization

Apps: Grid state estimation, fault detection, predictive asset failure, DER orchestration.
Benefits: Faster restoration, optimized capital deployment, reduced outages.

Microgrids & DER Integration

Apps: Real-time optimization of storage, generation, and loads; adaptive islanding; AI-driven control.
Benefits: Energy autonomy, improved resilience, higher renewable penetration.

Energy Storage (BESS)

Apps: Battery life prediction, cell-level monitoring, thermal management optimization.
Benefits: Longer asset life, safety, reduced O&M costs.

Solar Generation

Apps: PV output forecasting, panel soiling detection, inverter fault diagnosis.
Benefits: Increased yield, reduced downtime, better scheduling.

Wind Generation

Apps: Turbine performance optimization, blade damage detection, wind resource prediction.
Benefits: Higher capacity factor, extended turbine life.

CHP & Bridge Fuels

Apps: Combustion optimization, efficiency tuning, predictive maintenance.
Benefits: Lower fuel consumption, reduced emissions, improved economics.

Seaport Electrification

Apps: AI-driven shore power scheduling, berth allocation optimization, electrified cargo handling.
Benefits: Reduced emissions, faster turnaround, better resource allocation.

Airport Electrification

Apps: Ground operations optimization, autonomous equipment coordination, energy scheduling.
Benefits: Lower operational costs, improved passenger throughput.

Industrial Site Electrification

Apps: Process load optimization, energy-intensive equipment scheduling, emissions tracking.
Benefits: Lower peak demand, reduced carbon footprint, higher efficiency.

Technology Stack

Sensing & Data Acquisition

Tech: IoT sensors, SCADA systems, phasor measurement units (PMUs), satellite imagery, drones.
Examples: ABB Ability, Siemens SICAM, FLIR thermal cameras.

Data Integration & Edge Processing

Tech: Edge AI gateways, time-series databases, protocol converters.
Examples: NVIDIA EGX, Dell Edge Gateways, OSIsoft PI.

AI Models & Analytics

Tech: Machine learning for forecasting, reinforcement learning for optimization, computer vision for inspection.
Examples: AWS Forecast, Google Vertex AI, Azure ML.

Control Systems Integration

Tech: AI/ML integration with EMS, DERMS, SCADA, BMS.
Examples: Schneider EcoStruxure, Siemens Spectrum Power, AutoGrid.

Visualization & Decision Support

Tech: Digital twins, 3D geospatial dashboards, anomaly detection alerts.
Examples: GE Digital Twin, Bentley iTwin, Palantir Foundry.

Cybersecurity

Tech: AI-driven intrusion detection, anomaly monitoring, OT/IT segmentation.
Examples: Nozomi Networks, Claroty, Dragos.

Key bebefits of AI Deployment

• Predictive, not reactive — AI can forecast problems before they occur, preventing costly downtime.
• Integrated resource optimization — Ensures generation, storage, and loads are balanced in real time.
• Enhanced asset life — AI-driven monitoring extends lifespan and reduces replacement costs.
• Scalable efficiency — AI algorithms can adapt to small or large deployments with minimal reprogramming.
• Improved resilience — Critical for disaster recovery, black-start scenarios, and grid islanding.