Quickstart

Environment Setup

Before running [Fancy Tool Name], ensure that the necessary environment is prepared.

Using the provided script to activate the environment:

Simply execute activate_environment_windows.bat from the repository. This script will automatically create and activate the environment.
Manually installing the requirements:

To manually create and activate the environment, execute the following commands:
```
conda env create -f environment.yml
conda activate [your_environment_name]
```

Make sure to run all scripts and functions within the activated environment to ensure proper functionality.

Requirements

Python version: >= 3.8
Packages: All required packages are listed in the environment.yml file.

Running the Code

The easiest and fastest way to use the tool is via the Jupyter Notebook main.ipynb.

The complete workflow is illustrated step-by-step in the notebook. Start by running the notebook for the preconfigured standard location. If successful, you can modify the location to your region of interest.

For more details on available parameters and customizations, refer to the section Project Structure and Settings.

Using the Functions in a Python Script

If you prefer to use the modules directly in your own Python scripts, the following functions are available:

prepare_geodata.generate_complete_geodataset(case_study_name, location)

Generates a full geodata dataset for the specified location.

location can either be the name of a place known to OpenStreetMap or a geopolygon.
The resulting dataset is stored as a .geojson file in the folder named after case_study_name.

hd_time_series_generator.fast_TS_generator(case_study_name, True)

Generates heat demand time series for all buildings in the buildings.geojson file. The time series are saved as .csv files in the corresponding case_study_name directory.

clustering.perform_complete_clustering(case_study_name, scenario_name)

Clusters the buildings and proposes a district heating network. - The clustered data and network proposal are prepared for the optimization model. - Results are saved in the folder specified by scenario_name.

model.run_model(case_study_name, scenario_name)

Runs the complete optimization model to find the optimal heating configuration based on given options and parameters.

Results, along with figures, are stored in the folder named after scenario_name.
For further details about available settings and adjustments, see the section Project Structure and Settings.