Platform Features

Everything your grid needs.
Nothing it doesn't.

GridMind delivers a complete AI stack for energy grid intelligence — from raw sensor ingestion to automated regulatory compliance.

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Core Capabilities

Nine pillars of grid intelligence

🔮

Demand Forecasting

Temporal transformer models delivering 98.7% accuracy across 15-min to 72-hr horizons. Trained on 10 years of global grid telemetry.

Transformer AI

Real-Time Optimization

Reinforcement learning dispatch engine rebalances load across all nodes in under 50ms. Eliminates peak demand surcharges automatically.

Sub-50ms
🔗

Renewable Integration

Predicts solar and wind generation windows to maximize renewable utilization and eliminate curtailment waste before it occurs.

Green Grid
🌐

Federated Learning

Privacy-preserving distributed model training. Each utility contributes to global intelligence without sharing raw grid data.

Privacy-First
📊

CSRD / GRI Reporting

Automated ESG compliance reports meeting Article 8 CSRD, GRI 302, and GRI 305 standards. Audit-ready in minutes, not months.

Compliance
🧠

GPU-Accelerated Inference

TensorRT-optimized models running on NVIDIA H100 clusters. 12M inferences per hour with 47ms p50 latency at production scale.

NVIDIA H100
📡

Edge Node Intelligence

ONNX Runtime models deployed directly at substation level for local decision-making with microsecond response times.

Edge AI
🔒

Security & Compliance

SOC 2 Type II, ISO 27001, and NERC CIP compliant. End-to-end encryption with zero-trust network architecture.

SOC 2 Type II
🔌

Universal Integration

Native connectors for SCADA, OSIsoft PI, GE Grid Solutions, Siemens EMS, and all major utility management platforms.

No-Code Setup
Deep Dive

Demand forecasting that sets the standard

Our temporal transformer architecture processes 1,200+ grid variables simultaneously through multi-head self-attention layers — capturing complex temporal dependencies that traditional ARIMA and regression models fundamentally cannot.

Models are continuously retrained on live telemetry using PyTorch with automatic hyperparameter optimization — ensuring accuracy stays above 98% even as grid topology evolves.

  • Multi-horizon outputs: 15-min through 7-day
  • Uncertainty quantification with confidence intervals
  • Weather, market, and event feature integration
  • Automatic anomaly detection and alerting
Forecast Accuracy by Model Type
GridMind Transformer98.7%
Legacy ARIMA84.2%
Linear Regression78.1%
Random Forest89.5%
Model Parameters1.2B
Training Data Points847M
Inference Latency p5047ms
GPU Throughput12M/hr
Integrations

Works with your existing stack

Native connectors for every major utility platform, SCADA system, and cloud provider.

🔧
SCADA Systems
OPC-UA / DNP3
📈
OSIsoft PI
Historian
GE Grid Solutions
EMS / ADMS
🏭
Siemens EMS
Grid Control
☁️
AWS / Azure / GCP
Cloud Deploy
📊
Tableau / PowerBI
Visualization
🌤️
NOAA / ECMWF
Weather Data
🔌
EV Charging Networks
Demand Signals

See every feature in action

Schedule a live demo with our solutions team — customized to your grid topology and use case.