ESP32-Based Energy Meter: Train an Edge Impulse Model to Recognize Household Loads

Bringing AI to Your Energy Meter

This project shows how to enhance an ESP32-based smart energy meter by integrating Edge Impulse machine learning capabilities. The goal is to enable the device to identify and classify different household appliances based on their energy consumption patterns.

Data and Tools

To make this possible, Home Assistant serves as the primary data source. The energy usage data collected through it provides a foundation for training an AI model. Python-based labeling scripts are then used to prepare and clean the datasets before feeding them into Edge Impulse for model training.

Model Training and Deployment

Once prepared, the energy data is uploaded to Edge Impulse, where it is used to train a model capable of recognizing distinct electrical load signatures. After successful training, the model can be deployed back onto the ESP32, allowing real-time device classification directly at the edge without relying on cloud processing.

“Learn how to use Home Assistant data and Python labeling scripts to train an Edge Impulse model.”

Applications and Impact

This combination of low-cost hardware and AI modeling tools helps create a more efficient, privacy-friendly, and responsive home energy monitoring solution. By running inference locally, the system can provide immediate feedback and insights on energy consumption patterns.

Author’s summary:
A practical guide to building an AI-powered energy meter using ESP32, Home Assistant data, and Edge Impulse tools to recognize and classify home power loads in real time.

Elektor Magazine — 2025-12-01