openego /
eGon-data
| @@ 492-526 (lines=35) @@ | ||
| 489 | return dsm |
|
| 490 | ||
| 491 | ||
| 492 | def ind_osm_data_import_individual(ind_vent_cool_share): |
|
| 493 | """ |
|
| 494 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 495 | ||
| 496 | Parameters |
|
| 497 | ---------- |
|
| 498 | ind_share: float |
|
| 499 | Share of considered application in industry demand |
|
| 500 | """ |
|
| 501 | ||
| 502 | # import load data |
|
| 503 | ||
| 504 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 505 | "ind_osm_loadcurves_individual" |
|
| 506 | ] |
|
| 507 | ||
| 508 | dsm = db.select_dataframe( |
|
| 509 | f""" |
|
| 510 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
|
| 511 | {sources['schema']}.{sources['table']} |
|
| 512 | """ |
|
| 513 | ) |
|
| 514 | ||
| 515 | # calculate share of timeseries for cooling and ventilation out of |
|
| 516 | # industry-data |
|
| 517 | ||
| 518 | timeseries = dsm["p_set"].copy() |
|
| 519 | ||
| 520 | for index, liste in timeseries.items(): |
|
| 521 | share = [float(item) * ind_vent_cool_share for item in liste] |
|
| 522 | ||
| 523 | timeseries.loc[index] = share |
|
| 524 | ||
| 525 | dsm["p_set"] = timeseries.copy() |
|
| 526 | ||
| 527 | return dsm |
|
| 528 | ||
| 529 | ||
| @@ 454-485 (lines=32) @@ | ||
| 451 | return dsm |
|
| 452 | ||
| 453 | ||
| 454 | def ind_osm_data_import(ind_vent_cool_share): |
|
| 455 | """ |
|
| 456 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 457 | ||
| 458 | Parameters |
|
| 459 | ---------- |
|
| 460 | ind_share: float |
|
| 461 | Share of considered application in industry demand |
|
| 462 | """ |
|
| 463 | ||
| 464 | # import load data |
|
| 465 | ||
| 466 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 467 | "ind_osm_loadcurves" |
|
| 468 | ] |
|
| 469 | ||
| 470 | dsm = db.select_dataframe( |
|
| 471 | f""" |
|
| 472 | SELECT bus, scn_name, p_set FROM |
|
| 473 | {sources['schema']}.{sources['table']} |
|
| 474 | """ |
|
| 475 | ) |
|
| 476 | ||
| 477 | # calculate share of timeseries for cooling and ventilation out of |
|
| 478 | # industry-data |
|
| 479 | ||
| 480 | timeseries = dsm["p_set"].copy() |
|
| 481 | ||
| 482 | for index, liste in timeseries.items(): |
|
| 483 | share = [float(item) * ind_vent_cool_share for item in liste] |
|
| 484 | ||
| 485 | timeseries.loc[index] = share |
|
| 486 | ||
| 487 | dsm["p_set"] = timeseries.copy() |
|
| 488 | ||
