openego /
eGon-data
| @@ 450-484 (lines=35) @@ | ||
| 447 | return dsm |
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| 448 | ||
| 449 | ||
| 450 | def ind_osm_data_import_individual(ind_vent_cool_share): |
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| 451 | """ |
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| 452 | Import industry data per osm-area necessary to identify DSM-potential. |
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| 453 | ---------- |
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| 454 | ind_share: float |
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| 455 | Share of considered application in industry demand |
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| 456 | """ |
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| 457 | ||
| 458 | # import load data |
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| 459 | ||
| 460 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 461 | "ind_osm_loadcurves_individual" |
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| 462 | ] |
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| 463 | ||
| 464 | dsm = db.select_dataframe( |
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| 465 | f""" |
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| 466 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
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| 467 | {sources['schema']}.{sources['table']} |
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| 468 | """ |
|
| 469 | ) |
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| 470 | ||
| 471 | # calculate share of timeseries for cooling and ventilation out of |
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| 472 | # industry-data |
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| 473 | ||
| 474 | timeseries = dsm["p_set"].copy() |
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| 475 | ||
| 476 | for index, liste in timeseries.items(): |
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| 477 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 478 | ||
| 479 | timeseries.loc[index] = share |
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| 480 | ||
| 481 | dsm["p_set"] = timeseries.copy() |
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| 482 | ||
| 483 | return dsm |
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| 484 | ||
| 485 | ||
| 486 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 487 | """ |
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| @@ 414-445 (lines=32) @@ | ||
| 411 | return dsm |
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| 412 | ||
| 413 | ||
| 414 | def ind_osm_data_import(ind_vent_cool_share): |
|
| 415 | """ |
|
| 416 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 417 | ---------- |
|
| 418 | ind_share: float |
|
| 419 | Share of considered application in industry demand |
|
| 420 | """ |
|
| 421 | ||
| 422 | # import load data |
|
| 423 | ||
| 424 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 425 | "ind_osm_loadcurves" |
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| 426 | ] |
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| 427 | ||
| 428 | dsm = db.select_dataframe( |
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| 429 | f""" |
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| 430 | SELECT bus, scn_name, p_set FROM |
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| 431 | {sources['schema']}.{sources['table']} |
|
| 432 | """ |
|
| 433 | ) |
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| 434 | ||
| 435 | # calculate share of timeseries for cooling and ventilation out of |
|
| 436 | # industry-data |
|
| 437 | ||
| 438 | timeseries = dsm["p_set"].copy() |
|
| 439 | ||
| 440 | for index, liste in timeseries.items(): |
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| 441 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 442 | ||
| 443 | timeseries.loc[index] = share |
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| 444 | ||
| 445 | dsm["p_set"] = timeseries.copy() |
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| 446 | ||
| 447 | return dsm |
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| 448 | ||
