Skip to content

Analyzing and plotting data

The script plot.py generates various plots to visualize the historical electricity demand data and model results. It reads its configuration from config/plot_config.yaml by default. You can run the script using:

uv run plot.py

Set the figure field in config/plot_config.yaml to specify the type of plot to generate. Available options include:

  • map_of_available_entities to visualize the countries and subdivisions for which electricity demand data has been retrieved.
  • data_availability to visualize the availability of electricity demand data clustered by GDP, PPP per capita and annual electricity demand per capita.
  • ml_results to visualize the results of machine learning models and their error metrics.

Machine learning model results

The folders ml_models/validation/ and ml_models/cross_validation/ contain CSV files with the Mean Absolute Percentage Error (MAPE) results of different versions of the machine learning models used for electricity demand forecasting. The MAPE is the difference between the predicted and actual electricity demand, expressed as a percentage of the actual demand. Each file corresponds to a specific model and entity group.

To plot the results, update config/plot_config.yaml with the desired figure and version values, then run:

uv run plot.py

Examples of relevant configuration values:

  • figure: ml_results
  • version: <model_version>
  • compare_with_version: <other_model_version> (optional)
  • by_group: true (optional)

To use a different config file, pass it with -c:

uv run plot.py -c /path/to/plot_config.yaml

The resulting plots will be saved in the demandcast/figures directory.