openego /
eGon-data
| @@ 852-893 (lines=42) @@ | ||
| 849 | ) |
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| 850 | ||
| 851 | ||
| 852 | def get_peta_demand(mvgd, scenario): |
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| 853 | """ |
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| 854 | Retrieve annual peta heat demand for residential buildings for either |
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| 855 | eGon2035 or eGon100RE scenario. |
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| 856 | ||
| 857 | Parameters |
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| 858 | ---------- |
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| 859 | mvgd : int |
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| 860 | MV grid ID. |
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| 861 | scenario : str |
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| 862 | Possible options are eGon2035 or eGon100RE |
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| 863 | ||
| 864 | Returns |
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| 865 | ------- |
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| 866 | df_peta_demand : pd.DataFrame |
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| 867 | Annual residential heat demand per building and scenario. Columns of |
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| 868 | the dataframe are zensus_population_id and demand. |
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| 869 | ||
| 870 | """ |
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| 871 | ||
| 872 | with db.session_scope() as session: |
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| 873 | query = ( |
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| 874 | session.query( |
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| 875 | MapZensusGridDistricts.zensus_population_id, |
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| 876 | EgonPetaHeat.demand, |
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| 877 | ) |
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| 878 | .filter(MapZensusGridDistricts.bus_id == mvgd) |
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| 879 | .filter( |
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| 880 | MapZensusGridDistricts.zensus_population_id |
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| 881 | == EgonPetaHeat.zensus_population_id |
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| 882 | ) |
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| 883 | .filter( |
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| 884 | EgonPetaHeat.sector == "residential", |
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| 885 | EgonPetaHeat.scenario == scenario, |
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| 886 | ) |
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| 887 | ) |
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| 888 | ||
| 889 | df_peta_demand = pd.read_sql( |
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| 890 | query.statement, query.session.bind, index_col=None |
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| 891 | ) |
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| 892 | ||
| 893 | return df_peta_demand |
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| 894 | ||
| 895 | ||
| 896 | def get_residential_heat_profile_ids(mvgd): |
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| @@ 946-987 (lines=42) @@ | ||
| 943 | return df_demand_share |
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| 944 | ||
| 945 | ||
| 946 | def get_peta_demand(mvgd, scenario): |
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| 947 | """ |
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| 948 | Retrieve annual peta heat demand for CTS for either |
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| 949 | eGon2035 or eGon100RE scenario. |
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| 950 | ||
| 951 | Parameters |
|
| 952 | ---------- |
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| 953 | mvgd : int |
|
| 954 | ID of substation for which to get CTS demand. |
|
| 955 | scenario : str |
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| 956 | Possible options are eGon2035 or eGon100RE |
|
| 957 | ||
| 958 | Returns |
|
| 959 | ------- |
|
| 960 | df_peta_demand : pd.DataFrame |
|
| 961 | Annual residential heat demand per building and scenario. Columns of |
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| 962 | the dataframe are zensus_population_id and demand. |
|
| 963 | ||
| 964 | """ |
|
| 965 | ||
| 966 | with db.session_scope() as session: |
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| 967 | query = ( |
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| 968 | session.query( |
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| 969 | MapZensusGridDistricts.zensus_population_id, |
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| 970 | EgonPetaHeat.demand, |
|
| 971 | ) |
|
| 972 | .filter(MapZensusGridDistricts.bus_id == int(mvgd)) |
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| 973 | .filter( |
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| 974 | MapZensusGridDistricts.zensus_population_id |
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| 975 | == EgonPetaHeat.zensus_population_id |
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| 976 | ) |
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| 977 | .filter( |
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| 978 | EgonPetaHeat.sector == "service", |
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| 979 | EgonPetaHeat.scenario == scenario, |
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| 980 | ) |
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| 981 | ) |
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| 982 | ||
| 983 | df_peta_demand = pd.read_sql( |
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| 984 | query.statement, query.session.bind, index_col=None |
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| 985 | ) |
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| 986 | ||
| 987 | return df_peta_demand |
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| 988 | ||
| 989 | ||
| 990 | def calc_cts_building_profiles( |
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