openego /
eGon-data
| @@ 942-983 (lines=42) @@ | ||
| 939 | return df_demand_share |
|
| 940 | ||
| 941 | ||
| 942 | def get_peta_demand(mvgd, scenario): |
|
| 943 | """ |
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| 944 | Retrieve annual peta heat demand for CTS for either |
|
| 945 | eGon2035 or eGon100RE scenario. |
|
| 946 | ||
| 947 | Parameters |
|
| 948 | ---------- |
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| 949 | mvgd : int |
|
| 950 | ID of substation for which to get CTS demand. |
|
| 951 | scenario : str |
|
| 952 | Possible options are eGon2035 or eGon100RE |
|
| 953 | ||
| 954 | Returns |
|
| 955 | ------- |
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| 956 | df_peta_demand : pd.DataFrame |
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| 957 | Annual residential heat demand per building and scenario. Columns of |
|
| 958 | the dataframe are zensus_population_id and demand. |
|
| 959 | ||
| 960 | """ |
|
| 961 | ||
| 962 | with db.session_scope() as session: |
|
| 963 | query = ( |
|
| 964 | session.query( |
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| 965 | MapZensusGridDistricts.zensus_population_id, |
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| 966 | EgonPetaHeat.demand, |
|
| 967 | ) |
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| 968 | .filter(MapZensusGridDistricts.bus_id == int(mvgd)) |
|
| 969 | .filter( |
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| 970 | MapZensusGridDistricts.zensus_population_id |
|
| 971 | == EgonPetaHeat.zensus_population_id |
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| 972 | ) |
|
| 973 | .filter( |
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| 974 | EgonPetaHeat.sector == "service", |
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| 975 | EgonPetaHeat.scenario == scenario, |
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| 976 | ) |
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| 977 | ) |
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| 978 | ||
| 979 | df_peta_demand = pd.read_sql( |
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| 980 | query.statement, query.session.bind, index_col=None |
|
| 981 | ) |
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| 982 | ||
| 983 | return df_peta_demand |
|
| 984 | ||
| 985 | ||
| 986 | def calc_cts_building_profiles( |
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| @@ 819-860 (lines=42) @@ | ||
| 816 | ) |
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| 817 | ||
| 818 | ||
| 819 | def get_peta_demand(mvgd, scenario): |
|
| 820 | """ |
|
| 821 | Retrieve annual peta heat demand for residential buildings for either |
|
| 822 | eGon2035 or eGon100RE scenario. |
|
| 823 | ||
| 824 | Parameters |
|
| 825 | ---------- |
|
| 826 | mvgd : int |
|
| 827 | MV grid ID. |
|
| 828 | scenario : str |
|
| 829 | Possible options are eGon2035 or eGon100RE |
|
| 830 | ||
| 831 | Returns |
|
| 832 | ------- |
|
| 833 | df_peta_demand : pd.DataFrame |
|
| 834 | Annual residential heat demand per building and scenario. Columns of |
|
| 835 | the dataframe are zensus_population_id and demand. |
|
| 836 | ||
| 837 | """ |
|
| 838 | ||
| 839 | with db.session_scope() as session: |
|
| 840 | query = ( |
|
| 841 | session.query( |
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| 842 | MapZensusGridDistricts.zensus_population_id, |
|
| 843 | EgonPetaHeat.demand, |
|
| 844 | ) |
|
| 845 | .filter(MapZensusGridDistricts.bus_id == mvgd) |
|
| 846 | .filter( |
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| 847 | MapZensusGridDistricts.zensus_population_id |
|
| 848 | == EgonPetaHeat.zensus_population_id |
|
| 849 | ) |
|
| 850 | .filter( |
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| 851 | EgonPetaHeat.sector == "residential", |
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| 852 | EgonPetaHeat.scenario == scenario, |
|
| 853 | ) |
|
| 854 | ) |
|
| 855 | ||
| 856 | df_peta_demand = pd.read_sql( |
|
| 857 | query.statement, query.session.bind, index_col=None |
|
| 858 | ) |
|
| 859 | ||
| 860 | return df_peta_demand |
|
| 861 | ||
| 862 | ||
| 863 | def get_residential_heat_profile_ids(mvgd): |
|
