openego /
eGon-data
| @@ 489-523 (lines=35) @@ | ||
| 486 | return dsm |
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| 487 | ||
| 488 | ||
| 489 | def ind_osm_data_import_individual(ind_vent_cool_share): |
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| 490 | """ |
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| 491 | Import industry data per osm-area necessary to identify DSM-potential. |
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| 492 | ---------- |
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| 493 | ind_share: float |
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| 494 | Share of considered application in industry demand |
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| 495 | """ |
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| 496 | ||
| 497 | # import load data |
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| 498 | ||
| 499 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 500 | "ind_osm_loadcurves_individual" |
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| 501 | ] |
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| 502 | ||
| 503 | dsm = db.select_dataframe( |
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| 504 | f""" |
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| 505 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
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| 506 | {sources['schema']}.{sources['table']} |
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| 507 | """ |
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| 508 | ) |
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| 509 | ||
| 510 | # calculate share of timeseries for cooling and ventilation out of |
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| 511 | # industry-data |
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| 512 | ||
| 513 | timeseries = dsm["p_set"].copy() |
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| 514 | ||
| 515 | for index, liste in timeseries.items(): |
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| 516 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 517 | ||
| 518 | timeseries.loc[index] = share |
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| 519 | ||
| 520 | dsm["p_set"] = timeseries.copy() |
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| 521 | ||
| 522 | return dsm |
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| 523 | ||
| 524 | ||
| 525 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 526 | """ |
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| @@ 453-484 (lines=32) @@ | ||
| 450 | return dsm |
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| 451 | ||
| 452 | ||
| 453 | def ind_osm_data_import(ind_vent_cool_share): |
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| 454 | """ |
|
| 455 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 456 | ---------- |
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| 457 | ind_share: float |
|
| 458 | Share of considered application in industry demand |
|
| 459 | """ |
|
| 460 | ||
| 461 | # import load data |
|
| 462 | ||
| 463 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 464 | "ind_osm_loadcurves" |
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| 465 | ] |
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| 466 | ||
| 467 | dsm = db.select_dataframe( |
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| 468 | f""" |
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| 469 | SELECT bus, scn_name, p_set FROM |
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| 470 | {sources['schema']}.{sources['table']} |
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| 471 | """ |
|
| 472 | ) |
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| 473 | ||
| 474 | # calculate share of timeseries for cooling and ventilation out of |
|
| 475 | # industry-data |
|
| 476 | ||
| 477 | timeseries = dsm["p_set"].copy() |
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| 478 | ||
| 479 | for index, liste in timeseries.items(): |
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| 480 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 481 | ||
| 482 | timeseries.loc[index] = share |
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| 483 | ||
| 484 | dsm["p_set"] = timeseries.copy() |
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| 485 | ||
| 486 | return dsm |
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| 487 | ||
