A Raspberry Pi Zero energy-monitoring project that reads current/voltage/power from an INA219 sensor, streams the data to InfluxDB, and visualizes it in Grafana — with a relay on GPIO for load control.

Plugjuice turns a Raspberry Pi Zero into a low-cost power-monitoring node. An INA219 current sensor measures the consumption of a connected load over I2C; a Python script periodically writes the readings to a local InfluxDB time-series database; Grafana then dashboards the data in real time. A separate relay script demonstrates switching a load on/off via GPIO.

• 📡 Current, voltage & power sensing via the Adafruit INA219 (I2C)
• 🗄️ Time-series storage in InfluxDB (consommation_energie measurement)
• 📊 Real-time dashboards in Grafana
• 🔌 Relay control over a load through a GPIO pin (BOARD pin 13)
• 🪶 Lightweight — designed to run on Raspberry Pi Zero with Raspberry Pi OS Lite

• Raspberry Pi Zero (or any Pi with I2C + GPIO)
• Adafruit INA219 breakout board
• Relay module
• The load you want to monitor (and optionally switch)

Verify the I2C device is visible: sudo i2cdetect -y 1 — the INA219 typically shows up at address 0x40.

git clone https://github.com/Gaston202/Plugjuice.git
cd Plugjuice

sudo raspi-config   # Interfacing Options → I2C → enable, then reboot
sudo apt-get install -y i2c-tools
i2cdetect -y 1       # confirm the INA219 appears (default 0x40)

pip install adafruit-circuitpython-ina219
# CircuitPython busio/board are part of the bundle and usually preinstalled

sudo apt-get install -y python3-rpi.gpio
sudo apt-get install -y python3-smbus
sudo apt-get install -y python3-influxdb

sudo apt-get install -y influxdb
sudo systemctl unmask influxdb.service
sudo systemctl start influxdb
sudo systemctl enable influxdb.service

Create the database that send_data.py writes to (default name ma_base_de_donnees):

influx
> CREATE DATABASE ma_base_de_donnees
> exit

# The original README installs a specific old build:
sudo dpkg -i --force-all grafana_6.0.1_armhf.deb
sudo service grafana-server start
# (Recommended: install a current Grafana release from the official APT repo instead.)

Open Grafana at http://<pi-ip>:3000, add InfluxDB as a data source (database ma_base_de_donnees), and build a dashboard querying the consommation_energie measurement.

The InfluxDB connection is defined at the top of send_data.py — adjust to match your setup:

hote_influxdb = "localhost"
port_influxdb = 8086
base_de_donnees_influxdb = "ma_base_de_donnees"
mesure_influxdb = "consommation_energie"

Run the scripts:

python read_current.py   # quick sanity check of sensor readings
python send_data.py       # start streaming to InfluxDB (Ctrl+C to stop)
python relay.py           # test the relay toggle

INA219 (I2C) ──► Raspberry Pi Zero ──► send_data.py ──► InfluxDB ──► Grafana dashboard
                                        (1 s interval)   (localhost:8086)
            relay.py ──► GPIO pin 13 ──► relay ──► load

• Unit bug in send_data.py — the comment says "Convert mA to A" but the function returns ina219.current unchanged (which is in mA), so the value stored in InfluxDB is in milliamps. Either divide by 1000 to store amps, or update the field name/comment to reflect mA.
• Hardcoded DB config — move InfluxDB host/database/measurement into environment variables or a config.py/.env file; avoid committing real credentials.
• No requirements.txt — add one (or a Pipfile) to make setup reproducible.
• Relay is not integrated with the sensing loop — wire relay control to a threshold (e.g., cut power above N W).
• Grafana version — the README pins Grafana 6.0.1 (2019); install a current release instead.
• Add a ready-to-import Grafana dashboard JSON.

This is a personal/learning project, but contributions are welcome.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is open-source and available under the MIT License.

Gaston — GitHub @Gaston202

Built as a Raspberry Pi IoT project to practice hardware sensing, time-series databases, and data visualization.