A collection of Jupyter notebooks and a small Python script for analysing and monitoring home energy data.

• electricity_usage.ipynb – Parses meter_readings.txt from an energy provider and charts electricity use by year and month.
• emonpi_stats.ipynb – Loads optical pulse data from an emonPi logger and converts it into power readings for short‑term usage graphs.
• weather_forecast.ipynb – Fetches temperature forecasts from OpenWeatherMap and visualises them with matplotlib and seaborn.
• resol_solar_reading.py – Connects to a Resol KM2/DeltaSol CS4 controller and prints solar heating values using a minimal implementation of the VBus protocol.

The notebooks expect Python 3 with the standard data-science stack installed:

pandas numpy matplotlib seaborn dateparser requests 

Any of the notebooks can be opened in JupyterLab or another notebook environment and run cell by cell. meter_readings.txt provides example electricity readings used by electricity_usage.ipynb.

The resol_solar_reading.py script is standalone and relies only on the Python standard library. Invoke it with the IP address and password of your KM2 device:

python resol_solar_reading.py -v 192.168.1.100 yourpassword

The optional -v flag prints each step of the VBus communication while the script waits for data frames.

meter_readings.txt contains chronological readings in the following form:

21 Jul 2021You gave129990 06 Jul 2021You gave129062 

These values are parsed into a DataFrame for further analysis and visualisation.

This repository does not specify a licence. Use at your own discretion.