openego /
eGon-data
| @@ 248-282 (lines=35) @@ | ||
| 245 | return dsm |
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| 246 | ||
| 247 | ||
| 248 | def ind_osm_data_import_individual(ind_vent_cool_share): |
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| 249 | """ |
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| 250 | Import industry data per osm-area necessary to identify DSM-potential. |
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| 251 | ---------- |
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| 252 | ind_share: float |
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| 253 | Share of considered application in industry demand |
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| 254 | """ |
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| 255 | ||
| 256 | # import load data |
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| 257 | ||
| 258 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 259 | "ind_osm_loadcurves_individual" |
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| 260 | ] |
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| 261 | ||
| 262 | dsm = db.select_dataframe( |
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| 263 | f""" |
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| 264 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
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| 265 | {sources['schema']}.{sources['table']} |
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| 266 | """ |
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| 267 | ) |
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| 268 | ||
| 269 | # calculate share of timeseries for cooling and ventilation out of |
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| 270 | # industry-data |
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| 271 | ||
| 272 | timeseries = dsm["p_set"].copy() |
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| 273 | ||
| 274 | for index, liste in timeseries.items(): |
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| 275 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 276 | ||
| 277 | timeseries.loc[index] = share |
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| 278 | ||
| 279 | dsm["p_set"] = timeseries.copy() |
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| 280 | ||
| 281 | return dsm |
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| 282 | ||
| 283 | ||
| 284 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 285 | """ |
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| @@ 212-243 (lines=32) @@ | ||
| 209 | return dsm |
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| 210 | ||
| 211 | ||
| 212 | def ind_osm_data_import(ind_vent_cool_share): |
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| 213 | """ |
|
| 214 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 215 | ---------- |
|
| 216 | ind_share: float |
|
| 217 | Share of considered application in industry demand |
|
| 218 | """ |
|
| 219 | ||
| 220 | # import load data |
|
| 221 | ||
| 222 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 223 | "ind_osm_loadcurves" |
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| 224 | ] |
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| 225 | ||
| 226 | dsm = db.select_dataframe( |
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| 227 | f""" |
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| 228 | SELECT bus, scn_name, p_set FROM |
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| 229 | {sources['schema']}.{sources['table']} |
|
| 230 | """ |
|
| 231 | ) |
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| 232 | ||
| 233 | # calculate share of timeseries for cooling and ventilation out of |
|
| 234 | # industry-data |
|
| 235 | ||
| 236 | timeseries = dsm["p_set"].copy() |
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| 237 | ||
| 238 | for index, liste in timeseries.items(): |
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| 239 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 240 | ||
| 241 | timeseries.loc[index] = share |
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| 242 | ||
| 243 | dsm["p_set"] = timeseries.copy() |
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| 244 | ||
| 245 | return dsm |
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| 246 | ||
