Christopher Mendez / AI Meter Public
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Christopher Mendez / AI Meter

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

About this project

Brief

This project consists on a "Smart Energy Meter" that is capable to identify and segregate loads and appliances connected to our house grid by analysing their harmonics behaviour extracted from their current and voltage raw signals, also, with some custom code, the microcontroller shares the inferences results and energy measurements to a server in the cloud using a WiFi Notecard

Portada Hackster.png

Hardware used

  • YHDC CT 100A:50mA
  • YHDC PT 230-9 VAC
  • ST Nucleo-F767ZI Dev Board
  • Custom PCB

Hackster project guide

Read a detailed tutorial here

Creating your first impulse (100% complete)

Acquire data

Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.

Let's collect some data

Design an impulse

Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.

Getting started: Continuous motion recognition
Getting started: Responding to your voice
Getting started: Adding sight to your sensors

Deploy

Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.

Deploy your model

Download block output

Title Type Size
Spectral features training data NPY file 133760 windows
Spectral features training labels NPY file 133760 windows
Spectral features testing data NPY file 3415 windows
Spectral features testing labels NPY file 3415 windows
Raw data training data NPY file 133760 windows
Raw data training labels NPY file 133760 windows
Raw data testing data NPY file 3415 windows
Raw data testing labels NPY file 3415 windows
Flatten training data NPY file 133760 windows
Flatten training labels NPY file 133760 windows
Flatten testing data NPY file 3415 windows
Flatten testing labels NPY file 3415 windows
NN Classifier model TensorFlow Lite (float32) 18 KB
NN Classifier model TensorFlow Lite (int8 quantized) 7 KB
NN Classifier model TensorFlow Lite (int8 quantized with float32 input and output) 7 KB
NN Classifier model TensorFlow SavedModel 28 KB
Anomaly detection model JSON 144 KB

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Summary

Data collected
2h 12m 9s

Project info

Project ID 26079
Project version 13
License Apache 2.0