openego /
eGon-data
| @@ 939-980 (lines=42) @@ | ||
| 936 | return df_demand_share |
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| 937 | ||
| 938 | ||
| 939 | def get_peta_demand(mvgd, scenario): |
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| 940 | """ |
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| 941 | Retrieve annual peta heat demand for CTS for either |
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| 942 | eGon2035 or eGon100RE scenario. |
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| 943 | ||
| 944 | Parameters |
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| 945 | ---------- |
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| 946 | mvgd : int |
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| 947 | ID of substation for which to get CTS demand. |
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| 948 | scenario : str |
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| 949 | Possible options are eGon2035 or eGon100RE |
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| 950 | ||
| 951 | Returns |
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| 952 | ------- |
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| 953 | df_peta_demand : pd.DataFrame |
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| 954 | Annual residential heat demand per building and scenario. Columns of |
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| 955 | the dataframe are zensus_population_id and demand. |
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| 956 | ||
| 957 | """ |
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| 958 | ||
| 959 | with db.session_scope() as session: |
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| 960 | query = ( |
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| 961 | session.query( |
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| 962 | MapZensusGridDistricts.zensus_population_id, |
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| 963 | EgonPetaHeat.demand, |
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| 964 | ) |
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| 965 | .filter(MapZensusGridDistricts.bus_id == int(mvgd)) |
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| 966 | .filter( |
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| 967 | MapZensusGridDistricts.zensus_population_id |
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| 968 | == EgonPetaHeat.zensus_population_id |
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| 969 | ) |
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| 970 | .filter( |
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| 971 | EgonPetaHeat.sector == "service", |
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| 972 | EgonPetaHeat.scenario == scenario, |
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| 973 | ) |
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| 974 | ) |
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| 975 | ||
| 976 | df_peta_demand = pd.read_sql( |
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| 977 | query.statement, query.session.bind, index_col=None |
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| 978 | ) |
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| 979 | ||
| 980 | return df_peta_demand |
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| 981 | ||
| 982 | ||
| 983 | def calc_cts_building_profiles( |
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| @@ 589-630 (lines=42) @@ | ||
| 586 | ) |
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| 587 | ||
| 588 | ||
| 589 | def get_peta_demand(mvgd, scenario): |
|
| 590 | """ |
|
| 591 | Retrieve annual peta heat demand for residential buildings for either |
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| 592 | eGon2035 or eGon100RE scenario. |
|
| 593 | ||
| 594 | Parameters |
|
| 595 | ---------- |
|
| 596 | mvgd : int |
|
| 597 | MV grid ID. |
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| 598 | scenario : str |
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| 599 | Possible options are eGon2035 or eGon100RE |
|
| 600 | ||
| 601 | Returns |
|
| 602 | ------- |
|
| 603 | df_peta_demand : pd.DataFrame |
|
| 604 | Annual residential heat demand per building and scenario. Columns of |
|
| 605 | the dataframe are zensus_population_id and demand. |
|
| 606 | ||
| 607 | """ |
|
| 608 | ||
| 609 | with db.session_scope() as session: |
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| 610 | query = ( |
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| 611 | session.query( |
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| 612 | MapZensusGridDistricts.zensus_population_id, |
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| 613 | EgonPetaHeat.demand, |
|
| 614 | ) |
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| 615 | .filter(MapZensusGridDistricts.bus_id == mvgd) |
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| 616 | .filter( |
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| 617 | MapZensusGridDistricts.zensus_population_id |
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| 618 | == EgonPetaHeat.zensus_population_id |
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| 619 | ) |
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| 620 | .filter( |
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| 621 | EgonPetaHeat.sector == "residential", |
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| 622 | EgonPetaHeat.scenario == scenario, |
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| 623 | ) |
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| 624 | ) |
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| 625 | ||
| 626 | df_peta_demand = pd.read_sql( |
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| 627 | query.statement, query.session.bind, index_col=None |
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| 628 | ) |
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| 629 | ||
| 630 | return df_peta_demand |
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| 631 | ||
| 632 | ||
| 633 | def get_residential_heat_profile_ids(mvgd): |
|
