openego /
eGon-data
| @@ 494-528 (lines=35) @@ | ||
| 491 | return dsm |
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| 492 | ||
| 493 | ||
| 494 | def ind_osm_data_import_individual(ind_vent_cool_share): |
|
| 495 | """ |
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| 496 | Import industry data per osm-area necessary to identify DSM-potential. |
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| 497 | ||
| 498 | Parameters |
|
| 499 | ---------- |
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| 500 | ind_share: float |
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| 501 | Share of considered application in industry demand |
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| 502 | """ |
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| 503 | ||
| 504 | # import load data |
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| 505 | ||
| 506 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 507 | "ind_osm_loadcurves_individual" |
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| 508 | ] |
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| 509 | ||
| 510 | dsm = db.select_dataframe( |
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| 511 | f""" |
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| 512 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
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| 513 | {sources['schema']}.{sources['table']} |
|
| 514 | """ |
|
| 515 | ) |
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| 516 | ||
| 517 | # calculate share of timeseries for cooling and ventilation out of |
|
| 518 | # industry-data |
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| 519 | ||
| 520 | timeseries = dsm["p_set"].copy() |
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| 521 | ||
| 522 | for index, liste in timeseries.items(): |
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| 523 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 524 | ||
| 525 | timeseries.loc[index] = share |
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| 526 | ||
| 527 | dsm["p_set"] = timeseries.copy() |
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| 528 | ||
| 529 | return dsm |
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| 530 | ||
| 531 | ||
| @@ 456-487 (lines=32) @@ | ||
| 453 | return dsm |
|
| 454 | ||
| 455 | ||
| 456 | def ind_osm_data_import(ind_vent_cool_share): |
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| 457 | """ |
|
| 458 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 459 | ||
| 460 | Parameters |
|
| 461 | ---------- |
|
| 462 | ind_share: float |
|
| 463 | Share of considered application in industry demand |
|
| 464 | """ |
|
| 465 | ||
| 466 | # import load data |
|
| 467 | ||
| 468 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 469 | "ind_osm_loadcurves" |
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| 470 | ] |
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| 471 | ||
| 472 | dsm = db.select_dataframe( |
|
| 473 | f""" |
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| 474 | SELECT bus, scn_name, p_set FROM |
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| 475 | {sources['schema']}.{sources['table']} |
|
| 476 | """ |
|
| 477 | ) |
|
| 478 | ||
| 479 | # calculate share of timeseries for cooling and ventilation out of |
|
| 480 | # industry-data |
|
| 481 | ||
| 482 | timeseries = dsm["p_set"].copy() |
|
| 483 | ||
| 484 | for index, liste in timeseries.items(): |
|
| 485 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 486 | ||
| 487 | timeseries.loc[index] = share |
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| 488 | ||
| 489 | dsm["p_set"] = timeseries.copy() |
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| 490 | ||
