openego /
eGon-data
| @@ 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 |
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| 954 | ID of substation for which to get CTS demand. |
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| 955 | scenario : str |
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| 956 | Possible options are eGon2035 or eGon100RE |
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| 957 | ||
| 958 | Returns |
|
| 959 | ------- |
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| 960 | df_peta_demand : pd.DataFrame |
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| 961 | Annual residential heat demand per building and scenario. Columns of |
|
| 962 | the dataframe are zensus_population_id and demand. |
|
| 963 | ||
| 964 | """ |
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| 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 | ) |
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| 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 |
|
| 985 | ) |
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| 986 | ||
| 987 | return df_peta_demand |
|
| 988 | ||
| 989 | ||
| 990 | def calc_cts_building_profiles( |
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| @@ 874-915 (lines=42) @@ | ||
| 871 | ) |
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| 872 | ||
| 873 | ||
| 874 | def get_peta_demand(mvgd, scenario): |
|
| 875 | """ |
|
| 876 | Retrieve annual peta heat demand for residential buildings for either |
|
| 877 | eGon2035 or eGon100RE scenario. |
|
| 878 | ||
| 879 | Parameters |
|
| 880 | ---------- |
|
| 881 | mvgd : int |
|
| 882 | MV grid ID. |
|
| 883 | scenario : str |
|
| 884 | Possible options are eGon2035 or eGon100RE |
|
| 885 | ||
| 886 | Returns |
|
| 887 | ------- |
|
| 888 | df_peta_demand : pd.DataFrame |
|
| 889 | Annual residential heat demand per building and scenario. Columns of |
|
| 890 | the dataframe are zensus_population_id and demand. |
|
| 891 | ||
| 892 | """ |
|
| 893 | ||
| 894 | with db.session_scope() as session: |
|
| 895 | query = ( |
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| 896 | session.query( |
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| 897 | MapZensusGridDistricts.zensus_population_id, |
|
| 898 | EgonPetaHeat.demand, |
|
| 899 | ) |
|
| 900 | .filter(MapZensusGridDistricts.bus_id == mvgd) |
|
| 901 | .filter( |
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| 902 | MapZensusGridDistricts.zensus_population_id |
|
| 903 | == EgonPetaHeat.zensus_population_id |
|
| 904 | ) |
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| 905 | .filter( |
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| 906 | EgonPetaHeat.sector == "residential", |
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| 907 | EgonPetaHeat.scenario == scenario, |
|
| 908 | ) |
|
| 909 | ) |
|
| 910 | ||
| 911 | df_peta_demand = pd.read_sql( |
|
| 912 | query.statement, query.session.bind, index_col=None |
|
| 913 | ) |
|
| 914 | ||
| 915 | return df_peta_demand |
|
| 916 | ||
| 917 | ||
| 918 | def get_residential_heat_profile_ids(mvgd): |
|
