openego /
eGon-data
| @@ 569-604 (lines=36) @@ | ||
| 566 | return dsm |
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| 567 | ||
| 568 | ||
| 569 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 570 | """ |
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| 571 | Import industry sites necessary to identify DSM-potential. |
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| 572 | ||
| 573 | Parameters |
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| 574 | ---------- |
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| 575 | ind_vent_share: float |
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| 576 | Share of considered application in industry demand |
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| 577 | wz: int |
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| 578 | Wirtschaftszweig to be considered within industry sites |
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| 579 | """ |
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| 580 | ||
| 581 | # import load data |
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| 582 | ||
| 583 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 584 | "ind_sites_loadcurves_individual" |
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| 585 | ] |
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| 586 | ||
| 587 | dsm = db.select_dataframe( |
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| 588 | f""" |
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| 589 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 590 | {sources['schema']}.{sources['table']} |
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| 591 | WHERE wz = {wz} |
|
| 592 | """ |
|
| 593 | ) |
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| 594 | ||
| 595 | # calculate share of timeseries for ventilation |
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| 596 | ||
| 597 | timeseries = dsm["p_set"].copy() |
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| 598 | ||
| 599 | for index, liste in timeseries.items(): |
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| 600 | share = [float(item) * ind_vent_share for item in liste] |
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| 601 | timeseries.loc[index] = share |
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| 602 | ||
| 603 | dsm["p_set"] = timeseries.copy() |
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| 604 | ||
| 605 | return dsm |
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| 606 | ||
| 607 | ||
| @@ 530-564 (lines=35) @@ | ||
| 527 | return dsm |
|
| 528 | ||
| 529 | ||
| 530 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 531 | """ |
|
| 532 | Import industry sites necessary to identify DSM-potential. |
|
| 533 | ||
| 534 | Parameters |
|
| 535 | ---------- |
|
| 536 | ind_vent_share: float |
|
| 537 | Share of considered application in industry demand |
|
| 538 | wz: int |
|
| 539 | Wirtschaftszweig to be considered within industry sites |
|
| 540 | """ |
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| 541 | ||
| 542 | # import load data |
|
| 543 | ||
| 544 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 545 | "ind_sites_loadcurves" |
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| 546 | ] |
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| 547 | ||
| 548 | dsm = db.select_dataframe( |
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| 549 | f""" |
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| 550 | SELECT bus, scn_name, p_set FROM |
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| 551 | {sources['schema']}.{sources['table']} |
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| 552 | WHERE wz = {wz} |
|
| 553 | """ |
|
| 554 | ) |
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| 555 | ||
| 556 | # calculate share of timeseries for ventilation |
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| 557 | ||
| 558 | timeseries = dsm["p_set"].copy() |
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| 559 | ||
| 560 | for index, liste in timeseries.items(): |
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| 561 | share = [float(item) * ind_vent_share for item in liste] |
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| 562 | timeseries.loc[index] = share |
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| 563 | ||
| 564 | dsm["p_set"] = timeseries.copy() |
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| 565 | ||
| 566 | return dsm |
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| 567 | ||
