hh_buildings

Household electricity demand time series for scenarios in 2035 and 2050 assigned to OSM-buildings.

class BuildingElectricityPeakLoads(**kwargs)

Bases: sqlalchemy.ext.declarative.api.Base

Class definition of table demand.egon_building_electricity_peak_loads.

Mapping of electricity demand time series and buildings including cell_id, building area and peak load. This table is created within hh_buildings.get_building_peak_loads().

building_id

peak_load_in_w

scenario

sector

voltage_level

class HouseholdElectricityProfilesOfBuildings(**kwargs)

Bases: sqlalchemy.ext.declarative.api.Base

Class definition of table demand.egon_household_electricity_profile_of_buildings.

Mapping of demand timeseries and buildings and cell_id. This table is created within hh_buildings.map_houseprofiles_to_buildings().

building_id

cell_id

id

profile_id

class OsmBuildingsSynthetic(**kwargs)

Bases: sqlalchemy.ext.declarative.api.Base

Class definition of table demand.osm_buildings_synthetic.

Lists generated synthetic building with id, zensus_population_id and building type. This table is created within hh_buildings.map_houseprofiles_to_buildings().

area

building

cell_id

geom_building

geom_point

id

n_amenities_inside

generate_mapping_table(egon_map_zensus_buildings_residential_synth, egon_hh_profile_in_zensus_cell)

Generate a mapping table for hh profiles to buildings.

All hh demand profiles are randomly assigned to buildings within the same cencus cell.

• profiles > buildings: buildings can have multiple profiles but every

  building gets at least one profile

• profiles < buildings: not every building gets a profile

Parameters:

• egon_map_zensus_buildings_residential_synth (pd.DataFrame) – Table with OSM and synthetic buildings ids per census cell
• egon_hh_profile_in_zensus_cell (pd.DataFrame) – Table mapping hh demand profiles to census cells

Returns:

pd.DataFrame – Table with mapping of profile ids to buildings with OSM ids

generate_synthetic_buildings(missing_buildings, edge_length)

Generate synthetic square buildings in census cells for every entry in missing_buildings.

Generate random placed synthetic buildings incl geom data within the bounds of the cencus cell. Buildings have each a square area with edge_length^2.

Parameters:

• missing_buildings (pd.Series or pd.DataFrame) – Table with cell_ids and building number
• edge_length (int) – Edge length of square synthetic building in meter

Returns:

pd.DataFrame – Table with generated synthetic buildings, area, cell_id and geom data

get_building_peak_loads()

Peak loads of buildings are determined.

Timeseries for every building are accumulated, the maximum value determined and with the respective nuts3 factor scaled for 2035 and 2050 scenario.

Note

In test-mode ‘SH’ the iteration takes place by ‘cell_id’ to avoid intensive RAM usage. For whole Germany ‘nuts3’ are taken and RAM > 32GB is necessary.

map_houseprofiles_to_buildings()

Cencus hh demand profiles are assigned to buildings via osm ids. If no OSM ids available, synthetic buildings are generated. A list of the generated buildings and supplementary data as well as the mapping table is stored in the db.

synthetic_buildings:

schema: openstreetmap tablename: osm_buildings_synthetic

mapping_profiles_to_buildings:

schema: demand tablename: egon_household_electricity_profile_of_buildings

Notes

match_osm_and_zensus_data(egon_hh_profile_in_zensus_cell, egon_map_zensus_buildings_residential)

Compares OSM buildings and census hh demand profiles.

OSM building data and hh demand profiles based on census data is compared. Census cells with only profiles but no osm-ids are identified to generate synthetic buildings. Census building count is used, if available, to define number of missing buildings. Otherwise, the overall mean profile/building rate is used to derive the number of buildings from the number of already generated demand profiles.

Parameters:

• egon_hh_profile_in_zensus_cell (pd.DataFrame) – Table mapping hh demand profiles to census cells
• egon_map_zensus_buildings_residential (pd.DataFrame) – Table with buildings osm-id and cell_id

Returns:

pd.DataFrame – Table with cell_ids and number of missing buildings

reduce_synthetic_buildings(mapping_profiles_to_buildings, synthetic_buildings)

Reduced list of synthetic buildings to amount actually used.

Not all are used, due to randomised assignment with replacing Id’s are adapted to continuous number sequence following openstreetmap.osm_buildings

class setup(dependencies)

Bases: egon.data.datasets.Dataset

Household electricity demand time series for scenarios in 2035 and 2050 assigned to OSM-buildings.

Assignment of household electricity demand timeseries to OSM buildings and generation of randomly placed synthetic 5x5m buildings if no sufficient OSM-data available in the respective census cell.

Dependencies

• houseprofiles_in_census_cells

Resulting tables

• OsmBuildingsSynthetic is created and filled
• HouseholdElectricityProfilesOfBuildings is created and filled
• BuildingElectricityPeakLoads is created and filled

The following datasets from the database are used for creation:

• demand.household_electricity_profiles_in_census_cells:

  Lists references and scaling parameters to time series data for each household in a cell by identifiers. This table is fundamental for creating subsequent data like demand profiles on MV grid level or for determining the peak load at load. Only the profile reference and the cell identifiers are used.

• society.egon_destatis_zensus_apartment_building_population_per_ha:

  Lists number of apartments, buildings and population for each census cell.

• boundaries.egon_map_zensus_buildings_residential:

  List of OSM tagged buildings which are considered to be residential.

What is the goal?

To assign every household demand timeseries, which already exist at cell level, to a specific OSM building.

What is the challenge?

The census and the OSM dataset differ from each other. The census uses statistical methods and therefore lacks accuracy at high spatial resolution. The OSM datasets is community based dataset which is extended throughout and does not claim to be complete. By merging these datasets inconsistencies need to be addressed. For example: not yet tagged buildings in OSM or new building areas not considered in census 2011.

How are these datasets combined?

The assignment of household demand timeseries to buildings takes place at cell level. Within each cell a pool of profiles exists, produced by the ‘HH Demand” module. These profiles are randomly assigned to a filtered list of OSM buildings within this cell. Every profile is assigned to a building and every building get a profile assigned if there is enough households by the census data. If there are more profiles than buildings, all additional profiles are randomly assigned. Therefore, multiple profiles can be assigned to one building, making it a multi-household building.

What are central assumptions during the data processing?

• Mapping zensus data to OSM data is not trivial. Discrepancies are substituted.
• Missing OSM buildings are generated by census building count.
• If no census building count data is available, the number of buildings is derived by an average rate of households/buildings applied to the number of households.

Drawbacks and limitations of the data

• Missing OSM buildings in cells without census building count are derived by an average rate of households/buildings applied to the number of households. As only whole houses can exist, the substitute is ceiled to the next higher integer. Ceiling is applied to avoid rounding to amount of 0 buildings.
• As this datasets is a cascade after profile assignement at census cells also check drawbacks and limitations in hh_profiles.py.

• Get a list with number of houses, households and household types per census cell

SELECT t1.cell_id, building_count, hh_count, hh_types FROM (
    SELECT
        cell_id,
        COUNT(DISTINCT(building_id)) AS building_count,
        COUNT(profile_id) AS hh_count
    FROM demand.egon_household_electricity_profile_of_buildings
    GROUP BY cell_id
) AS t1
FULL OUTER JOIN (
    SELECT
        cell_id,
        array_agg(
            array[CAST(hh_10types AS char), hh_type]
        ) AS hh_types
    FROM society.egon_destatis_zensus_household_per_ha_refined
    GROUP BY cell_id
) AS t2
ON t1.cell_id = t2.cell_id

name = 'Demand_Building_Assignment'

tasks = (<function map_houseprofiles_to_buildings>, <function get_building_peak_loads>)

version = '0.0.5'