Heat demands comprise space heating and dirinking hot water demands from 
residential and comertial trade and service (CTS) buildings.
Process heat demands from the industry are, depending on the required temperature
level, modelled as electrcity, hydrogen or methane demand. 

The spatial distribution of annual heat demands is taken from the Pan-European 
Thermal Altlas version 5.0.1 [Peta]_. 
This source provides data on annual european residential and CTS heat demands 
per hectar cell for the year 2015. 
In order to model future demands, the demand distribution extracted by Peta is 
then scaled to meet a national annual demand from external sources. 
The following national demands are taken for the selected scenarios: 

.. list-table:: Heat demands per sector and scenario
   :widths: 25 25 25 25
   :header-rows: 1

   * - 
     - Residential sector
     - CTS sector
     - Sources

   * - eGon2035
     - 379 TWh
     - 122 TWh
     - [Energiereferenzprognose]_

   * - eGon100RE
     - 284 TWh
     - 89 TWh
     - [Energiereferenzprognose]_


The resulting data is stored in the database table :py:class:`demand.egon_peta_heat <egon.data.datasets.heat_demand.EgonPetaHeat>`.
The implementation of these dataprocessing steps can be found in :py:class:`HeatDemandImport <egon.data.datasets.heat_demand.HeatDemandImport>`. 

Figure :ref:`residential-heat-demand-annual` shows the distribution of residential heat demands for scenario ``eGon2035``, 
categorized for different levels of annual demands.

.. figure:: /images/residential_heat_demand.png
  :name: residential-heat-demand-annual
  :width: 400
  
  Spatial distribution of residential heat demand in scenario ``eGon2035``


Afterwards, the annual heat demands are used to create hourly heat demand profiles. 
For residential heat demand profiles a pool of synthetical created bottom-up demand
profiles is used. Depending on the mean temperature per day, these profiles are 
randomly assigned to each residential building. The methodology is described in 
detail in [Buettner2022]_.


Data on residential heat demand profiles is stored in the database within the tables :py:class:`demand.egon_heat_timeseries_selected_profiles <egon.data.datasets.heat_demand_timeseries.idp_pool.EgonHeatTimeseries>`, :py:class:`demand.egon_daily_heat_demand_per_climate_zone <egon.data.datasets.heat_demand_timeseries.daily.EgonDailyHeatDemandPerClimateZone>`, :py:class:`boundaries.egon_map_zensus_climate_zones <egon.data.datasets.heat_demand_timeseries.daily.EgonMapZensusClimateZones>`. To create the profiles for a selected buidling, these tables 
have to be combined, e.g. like this: 

.. code-block:: none

   SELECT (b.demand/f.count * UNNEST(e.idp) * d.daily_demand_share)*1000 AS demand_profile
   FROM	(SELECT * FROM demand.egon_heat_timeseries_selected_profiles,
   UNNEST(selected_idp_profiles) WITH ORDINALITY as selected_idp) a
   JOIN demand.egon_peta_heat b
   ON b.zensus_population_id = a.zensus_population_id
   JOIN boundaries.egon_map_zensus_climate_zones c
   ON c.zensus_population_id = a.zensus_population_id
   JOIN demand.egon_daily_heat_demand_per_climate_zone d
   ON (c.climate_zone = d.climate_zone AND d.day_of_year = ordinality)
   JOIN demand.egon_heat_idp_pool e
   ON selected_idp = e.index
   JOIN (SELECT zensus_population_id, COUNT(building_id)
   FROM demand.egon_heat_timeseries_selected_profiles
   GROUP BY zensus_population_id
   ) f
   ON f.zensus_population_id = a.zensus_population_id
   WHERE a.building_id = SELECTED_BUILDING_ID
   AND b.scenario = 'eGon2035'
   AND b.sector = 'residential';


Exemplary resulting residential heat demand time series for a selected day in winter and 
summer considering different aggregation levels are visualized in figures :ref:`residential-heat-demand-timeseries-winter` and :ref:`residential-heat-demand-timeseries-summer`.

.. figure:: /images/residential_heat_demand_profile_winter.png
  :name: residential-heat-demand-timeseries-winter
  :width: 400
  
  Temporal distribution of residential heat demand for a selected day in winter
  
.. figure:: /images/residential_heat_demand_profile_summer.png
  :name: residential-heat-demand-timeseries-summer
  :width: 400
  
  Temporal distribution of residential heat demand for a selected day in summer

The temporal disaggregation of CTS heat demand is done using Standard Load Profiles Gas
from ``demandregio`` [demandregio]_ considering different profiles per CTS branch. 


The heat demand time series for both sectors creation is done in the Dataset 
:py:class:`HeatTimeSeries <egon.data.datasets.heat_demand_timeseries.HeatTimeSeries>`.