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@@ 240-274 (lines=35) @@
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return dsm |
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def ind_osm_data_import_individual(ind_vent_cool_share): |
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""" |
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Import industry data per osm-area necessary to identify DSM-potential. |
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---------- |
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ind_share: float |
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Share of considered application in industry demand |
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""" |
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# import load data |
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sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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"ind_osm_loadcurves_individual" |
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] |
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dsm = db.select_dataframe( |
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f""" |
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SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
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{sources['schema']}.{sources['table']} |
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WHERE scn_name in ('eGon2035', 'eGon100RE') |
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""" |
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) |
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# calculate share of timeseries for cooling and ventilation out of |
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# industry-data |
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timeseries = dsm["p_set"].copy() |
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for index, liste in timeseries.items(): |
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share = [float(item) * ind_vent_cool_share for item in liste] |
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timeseries.loc[index] = share |
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dsm["p_set"] = timeseries.copy() |
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return dsm |
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def ind_sites_vent_data_import(ind_vent_share, wz): |
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@@ 206-237 (lines=32) @@
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return dsm |
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def ind_osm_data_import(ind_vent_cool_share): |
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""" |
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Import industry data per osm-area necessary to identify DSM-potential. |
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---------- |
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ind_share: float |
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Share of considered application in industry demand |
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""" |
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# import load data |
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sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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"ind_osm_loadcurves" |
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] |
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dsm = db.select_dataframe( |
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f"""SELECT bus, scn_name, p_set FROM |
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{sources['schema']}.{sources['table']}""" |
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) |
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# calculate share of timeseries for cooling and ventilation out of |
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# industry-data |
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timeseries = dsm["p_set"].copy() |
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for index, liste in timeseries.items(): |
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share = [float(item) * ind_vent_cool_share for item in liste] |
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timeseries.loc[index] = share |
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dsm["p_set"] = timeseries.copy() |
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return dsm |
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def ind_osm_data_import_individual(ind_vent_cool_share): |
openego /
eGon-data
