data.datasets.heat_demand_timeseries.idp_pool.annual_demand_generator()

Complexity

Conditions

1

Size

Total Lines	62
Code Lines	15

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

from datetime import datetime
def annual_demand_generator(scenario):
    """
    Description: Create dataframe with annual demand and household count for each zensus cell

    Returns
    -------
    demand_count: pandas.DataFrame
        Annual demand of all zensus cell with MFH and SFH count and
        respective associated Station

    """

    demand_zone = db.select_dataframe(
        f"""
        SELECT a.demand, a.zensus_population_id, a.scenario, c.climate_zone
        FROM demand.egon_peta_heat a
        JOIN boundaries.egon_map_zensus_climate_zones c
        ON a.zensus_population_id = c.zensus_population_id
        WHERE a.sector = 'residential'
        AND a.scenario = '{scenario}'
        """,
        index_col="zensus_population_id",
    )

    house_count_MFH = db.select_dataframe(
        """

        SELECT cell_id as zensus_population_id, COUNT(*) as number FROM
        (
        SELECT cell_id, COUNT(*), building_id
        FROM demand.egon_household_electricity_profile_of_buildings
        GROUP BY (cell_id, building_id)
        ) a

        WHERE a.count >1
        GROUP BY cell_id
        """,
        index_col="zensus_population_id",
    )

    house_count_SFH = db.select_dataframe(
        """

        SELECT cell_id as zensus_population_id, COUNT(*) as number FROM
        (
        SELECT cell_id, COUNT(*), building_id
        FROM demand.egon_household_electricity_profile_of_buildings
        GROUP BY (cell_id, building_id)
        ) a
        WHERE a.count = 1
        GROUP BY cell_id
        """,
        index_col="zensus_population_id",
    )

    demand_zone["SFH"] = house_count_SFH.number
    demand_zone["MFH"] = house_count_MFH.number

    demand_zone["SFH"].fillna(0, inplace=True)
    demand_zone["MFH"].fillna(0, inplace=True)

    return demand_zone