industrial_sites

The central module containing all code dealing with the spatial distribution of industrial electricity demands. Industrial demands from DemandRegio are distributed from nuts3 level down to osm landuse polygons and/or industrial sites also identified within this processing step bringing three different inputs together.

class HotmapsIndustrialSites(**kwargs)[source]

Bases: sqlalchemy.orm.decl_api.Base

address

city

citycode

companyname

country

datasource

emissions_eprtr_2014

emissions_ets_2014

excess_heat_100_200C

excess_heat_200_500C

excess_heat_500C

excess_heat_total

fuel_demand

geom

location

production

siteid

sitename

subsector

wz

class IndustrialSites(**kwargs)[source]

Bases: sqlalchemy.orm.decl_api.Base

address

companyname

geom

id

nuts3

subsector

wz

class MergeIndustrialSites(dependencies)[source]

Bases: egon.data.datasets.Dataset

name: str: The name of the Dataset

version: str: The Dataset’s version. Can be anything from a simple semantic versioning string like “2.1.3”, to a more complex string, like for example “2021-01-01.schleswig-holstein.0” for OpenStreetMap data. Note that the latter encodes the Dataset’s date, region and a sequential number in case the data changes without the date or region changing, for example due to implementation changes.

class SchmidtIndustrialSites(**kwargs)[source]

Bases: sqlalchemy.orm.decl_api.Base

annual_tonnes

application

capacity_production

geom

id

landkreis_number

lat

lon

plant

wz

class SeenergiesIndustrialSites(**kwargs)[source]

Bases: sqlalchemy.orm.decl_api.Base

address

companyname

country

electricitydemand_tj

eu28

excess_heat

fueldemand_tj

geom

globalid

lat

level_1_pj

level_1_r_pj

level_1_r_tj

level_1_tj

level_2_pj

level_2_r_pj

level_2_r_tj

level_2_tj

level_3_pj

level_3_r_pj

level_3_r_tj

level_3_tj

lon

nuts1

nuts3

objectid

siteid

subsector

wz

create_tables()[source]: Create tables for industrial sites and distributed industrial demands :returns: None.

download_hotmaps()[source]: Download csv file on hotmap’s industrial sites.

download_import_industrial_sites()[source]

Wraps different functions to create tables, download csv files containing information on industrial sites in Germany and write this data to the local postgresql database

Returns: None.

download_seenergies()[source]: Download csv file on s-eenergies’ industrial sites.

hotmaps_to_postgres()[source]: Import hotmaps data to postgres database

map_nuts3()[source]

Match resulting industrial sites with nuts3 codes and fill column ‘nuts3’

Returns: None.

merge_inputs()[source]: Merge and clean data from different sources (hotmaps, seenergies, Thesis Schmidt)

schmidt_to_postgres()[source]: Import data from Thesis by Danielle Schmidt to postgres database

seenergies_to_postgres()[source]: Import seenergies data to postgres database