Grid AI - Lightweight Edge AI for Grid Monitoring

Transforming Grid Monitoring

Grid AI addresses critical challenges in electricity distribution networks by providing real-time monitoring of transformer health, enabling predictive maintenance, and optimizing grid operations.

Leveraging LoRaWAN's long-range, low-power capabilities combined with semantic data processing, our system delivers actionable insights that reduce downtime, prevent failures, and improve overall grid efficiency.

Key Benefits

Predictive Maintenance: Identify potential failures before they occur
Real-time Monitoring: Continuous health assessment of transformers
Cost Reduction: Minimize unplanned outages and maintenance costs
Energy Efficiency: Low-power sensors with years of battery life
Scalable Deployment: Easy to deploy across large distribution networks

Lightweight Edge AI

The model runs on the monitoring device itself. The device transmits the decision, not the waveform — which is why a battery-powered node sends under 40 bytes a day and still identifies phase faults with 99% accuracy.

99% Phase fault identification (M₁) Precision 0.98 · Recall 0.98 · F1 0.98
3,200× Yearly data reduction 55 MB → 17 KB per year
<40 B Daily communication Battery-powered, no mains supply needed

vs. threshold-based baseline

GridAI compared with threshold-based fault detection
Method	Accuracy	Daily data
Threshold-based	Relative^*	151 KB
Threshold + goal-oriented comm.	Relative^*	40 B
GridAI	~99%	<40 B

^* Load-dependent; no absolute accuracy guarantee.

Communication footprint

Communication footprint of continuous versus goal-oriented transmission
Mode	Per packet	Daily	Yearly
Continuous	105 B	151 KB	55 MB
Goal-oriented	15 B	40 B	17 KB
Reduction	7×	3,800×	3,200×

Source: Aydar, Baghaee, Firuzi, Uysal, Yildirim — GridAI: Edge AI Smart Grid Fault Detection with Goal-Oriented Communication, EdgeSys ’26 (ACM).

Technical Features

On-Device Inference

The fault-detection model runs on the monitoring device — no gateway-side compute and no cloud round-trip. Raw sensor data is interpreted where it is measured, and only the result is transmitted.

LoRaWAN Connectivity

Long-range wireless communication (up to 15km) with minimal power consumption, enabling deployment in remote locations.

Ultra-Low Power

Battery-powered sensors with 5-10 year lifespan, eliminating the need for external power sources or frequent maintenance.

Secure Communication

End-to-end encryption and secure authentication protocols protect critical infrastructure data from unauthorized access.

Analytics Dashboard

Intuitive web-based interface for monitoring, analysis, and reporting with customizable alerts and notifications.

Easy Integration

RESTful APIs and standard protocols enable seamless integration with existing SCADA and grid management systems.

Use Cases

Distribution Transformer Monitoring

Monitor temperature, load, voltage, and current in real-time to detect overloading, overheating, and other conditions that could lead to transformer failure.