clustering module

clustering.add_hot_water_demand(building_cluster: GeoDataFrame, cluster_TS: DataFrame, cost_parameter: dict) → DataFrame

Add a simplified hot water demand to the heat demand time series.

This function adds a simplified hot water demand to the heat demand time series of the buildings. The hot water demand is equally distributed over the year and is calculated based on the number of dwellings in each cluster. The hot water demand is added to the existing heat demand time series.

Parameters:

• building_cluster (gpd.GeoDataFrame) -- geodataframe with data for each cluster

• cluster_TS (pd.DataFrame) -- dataframe with heat demand time series of the clusters

• cost_parameter (dict) -- dictionary with cost parameters for the clustering

Returns:

dataframe with heat demand time serieswith added hot water demand in the same time series

Return type:

pd.DataFrame

clustering.cluster_buildings(gdf_building_data: GeoDataFrame, cost_parameter: dict) → GeoDataFrame

Cluster the buildings into clusters based on their location and aggregate/calculate properties for each cluster.

This implementation uses KMeans clustering to group buildings into clusters based on their geographical coordinates. A different clustering method can be used if needed.

Parameters:

• gdf_building_data (gpd.GeoDataFrame) -- geodataframe with building data, must contain a centroid column

• cost_parameter (dict) -- dictionary with cost parameters for the clustering

Returns:

geodataframe with data for each cluster for subsequent analysis

Return type:

gpd.GeoDataFrame

clustering.cluster_heat_demand(gdf_allheatnodes: GeoDataFrame, df_building_TS: DataFrame) → DataFrame

Cluster the heat demand time series of the buildings into clusters based on the spatial clustering of the buildings.

Parameters:

• gdf_allheatnodes (gpd.GeoDataFrame) -- geodataframe with data for each cluster and the heat generation units

• df_building_TS (pd.DataFrame) -- dataframe with heat demand time series of the buildings

Returns:

dataframe with heat demand time series of the clusters

Return type:

pd.DataFrame

clustering.get_centroids(gdf_building_data: GeoDataFrame)

Calculate the centroids of the buildings and add them to the geodataframe.

Parameters:

gdf_building_data (gpd.GeoDataFrame) -- geodataframe with building data, must contain a geometry column

clustering.merge_heatGenUnits(gdf_clustered_buildings: GeoDataFrame, gdf_heatGenUnits: GeoDataFrame) → GeoDataFrame

Merge the data for the heat generation units with the data for the clusters of buildings.

Parameters:

• gdf_clustered_buildings (gpd.GeoDataFrame) -- geodataframe with data for each building cluster

• gdf_heatGenUnits (gpd.GeoDataFrame) -- geodataframe with data for the heat generation units

Returns:

geodataframe with data for each cluster and the heat generation units

Return type:

gpd.GeoDataFrame

clustering.perform_complete_clustering(case_study_name: str, scenario_name: str) → None

Perform the complete clustering process and save the data for the optimisation model.

Parameters:

• case_study_name (str) -- name of the case study

• scenario_name (str) -- name of the scenario

clustering.propose_network(gdf_heat_nodes: GeoDataFrame) → GeoDataFrame

Propose a network of heat pipes between the clusters of building clusters.

The network is based on the delaunay triangulation and minimum spanning tree. A different network generation method can be used if needed.

Parameters:

gdf_heat_nodes (gpd.GeoDataFrame) -- geodataframe with data for each cluster

Returns:

geodataframe with data for the network

Return type:

gpd.GeoDataFrame

clustering.read_building_TS_from_disk(case_study_name: str, config: dict) → DataFrame

Read the building heat demand time series for a given casestudy from the disk and return a dataframe.

Parameters:

• case_study_name (str) -- name of the case study

• config (dict) -- configuration dictionary

Returns:

dataframe with building heat demand time series of the buildings

Return type:

pd.DataFrame

clustering.read_geo_data_from_disk(case_study_name: str, config: dict) → GeoDataFrame

Read the building data for a given casestudy from the disk and return a geodataframe.

Parameters:

• case_study_name (str) -- name of the case study

• config (dict) -- configuration dictionary

Returns:

geodataframe with building data

Return type:

gpd.GeoDataFrame

clustering.read_heatGenUnits_from_disk(case_study_name: str, scenario_name: str, config: dict) → GeoDataFrame

Read the heat generation units for a given casestudy from the disk and return a geodataframe.

Parameters:

• case_study_name (str) -- name of the case study

• scenario_name (str) -- name of the scenario

• config (dict) -- configuration dictionary

Returns:

geodataframe with heat generation units

Return type:

gpd.GeoDataFrame

clustering.read_wasteHeatProfiles_from_disk(gdf_heat_gen: GeoDataFrame, case_study_name: str, scenario_name: str, config: dict) → DataFrame

Read the waste heat profiles for a given casestudy from the disk and return a dataframe.

Parameters:

• gdf_heat_gen (gpd.GeoDataFrame) -- geodataframe with heat generation units

• case_study_name (str) -- name of the case study

• scenario_name (str) -- name of the scenario

• config (dict) -- configuration dictionary

Returns:

dataframe with waste heat profiles of the heat generation units

Return type:

pd.DataFrame

clustering.save_data(gdf_heat_nodes: GeoDataFrame, df_cluster_TS: DataFrame, gdf_heat_network: GeoDataFrame, gdf_heat_gen: GeoDataFrame, df_waste_heat_profiles: DataFrame, case_study_name: str, scenario_name: str, config: dict)

Save the data in the format for the optimisation model to the scenario folder.

Parameters:

• gdf_heat_nodes (gpd.GeoDataFrame) -- geodataframe with data for each cluster

• df_cluster_TS (pd.DataFrame) -- dataframe with heat demand time series of the clusters

• gdf_heat_network (gpd.GeoDataFrame) -- geodataframe with data for the network

• gdf_heat_gen (gpd.GeoDataFrame) -- geodataframe with data for the heat generation units

• df_waste_heat_profiles (pd.DataFrame) -- dataframe with waste heat profiles of the heat generation units

• case_study_name (str) -- name of the case study

• scenario_name (str) -- name of the scenario

• config (dict) -- configuration dictionary