GridMind

// Overview

Distributed intelligence for smart energy grids — optimising load balancing with adaptive control systems that learn, predict, and respond in real time.

Modern power grids were designed for a world of steady, predictable demand. That world no longer exists. The rise of renewable generation, EV charging loads, and distributed storage has made grid management exponentially harder — and the consequences of getting it wrong are serious.

GridMind approaches the problem differently. Instead of centralised control, it distributes lightweight AI agents across the grid — at substations, at inverters, at meters — each making local decisions while coordinating globally through a gossip protocol. The result is a system that is both faster and more resilient than traditional SCADA approaches.

// Performance Targets

DESIGNED FOR SCALE.

18%

Simulated Load Reduction

<50ms

Agent Response Latency

10k+

Simulated Grid Nodes

99.97%

Uptime in Simulation

// How It Works

THE ARCHITECTURE.

LAYER 01 //

Edge Agents

Lightweight ML models deployed on embedded hardware at each node. They monitor local conditions, predict short-term demand, and actuate control decisions in under 50ms — no cloud round-trip required.

LAYER 02 //

Coordination Layer

Agents communicate using a custom gossip protocol over the existing grid comms infrastructure. They share state, negotiate load shifts, and propagate anomaly signals without a central coordinator.

LAYER 03 //

Global Optimiser

A cloud-hosted reinforcement learning model trains continuously on aggregated grid data, updating agent policy weights overnight. Agents pull new weights at startup — no downtime, no manual updates.

// Core Capabilities

WHAT GRIDMIND DOES.

Predictive Load Balancing

Each agent forecasts local demand 15 minutes ahead using a compact LSTM model trained on historical consumption patterns, weather feeds, and calendar data. Mis-forecasts trigger peer consultation automatically.

Fault Isolation

When an agent detects anomalous voltage or current signatures consistent with a developing fault, it notifies its neighbours and pre-emptively reroutes load — before the fault cascades.

Renewable Integration

Solar and wind generation forecasts are fed to agents at the inverter level, allowing the grid to pre-position storage and adjust demand-side flexibility in anticipation of generation ramps.

Operator Dashboard

A real-time visualisation layer shows grid topology, agent states, active load shifts, and anomaly alerts. Operators can override any agent decision with a single click and audit the full decision log.

// Technology Stack

BUILT WITH.

Edge AI

TensorFlow Lite

Quantised LSTM models, runs on Cortex-M4 at 120MHz

Communication

Custom Gossip Protocol

UDP multicast, vector clocks, eventual consistency

Training

Ray RLlib + PyTorch

Multi-agent RL, distributed rollout workers

Simulation

OpenDSS + Pandapower

Full electrical grid simulation with agent-in-the-loop

Backend

Rust + Apache Kafka

High-throughput telemetry ingestion, sub-ms processing

Dashboard

React + D3.js

Live grid topology map, real-time agent state overlays

WORK WITH US
ON GRIDMIND.

We’re looking for grid operators, researchers, and energy-sector partners.

Get In Touch →Read the Paper

DESIGNED FOR SCALE.

THE ARCHITECTURE.

WHAT GRIDMIND DOES.

BUILT WITH.

WORK WITH USON GRIDMIND.

GRID
MIND

WORK WITH US
ON GRIDMIND.