openego /
eGon-data
| @@ 246-280 (lines=35) @@ | ||
| 243 | return dsm |
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| 244 | ||
| 245 | ||
| 246 | def ind_osm_data_import_individual(ind_vent_cool_share): |
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| 247 | """ |
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| 248 | Import industry data per osm-area necessary to identify DSM-potential. |
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| 249 | ---------- |
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| 250 | ind_share: float |
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| 251 | Share of considered application in industry demand |
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| 252 | """ |
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| 253 | ||
| 254 | # import load data |
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| 255 | ||
| 256 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 257 | "ind_osm_loadcurves_individual" |
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| 258 | ] |
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| 259 | ||
| 260 | dsm = db.select_dataframe( |
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| 261 | f""" |
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| 262 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
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| 263 | {sources['schema']}.{sources['table']} |
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| 264 | """ |
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| 265 | ) |
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| 266 | ||
| 267 | # calculate share of timeseries for cooling and ventilation out of |
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| 268 | # industry-data |
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| 269 | ||
| 270 | timeseries = dsm["p_set"].copy() |
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| 271 | ||
| 272 | for index, liste in timeseries.items(): |
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| 273 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 274 | ||
| 275 | timeseries.loc[index] = share |
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| 276 | ||
| 277 | dsm["p_set"] = timeseries.copy() |
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| 278 | ||
| 279 | return dsm |
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| 280 | ||
| 281 | ||
| 282 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 283 | """ |
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| @@ 210-241 (lines=32) @@ | ||
| 207 | return dsm |
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| 208 | ||
| 209 | ||
| 210 | def ind_osm_data_import(ind_vent_cool_share): |
|
| 211 | """ |
|
| 212 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 213 | ---------- |
|
| 214 | ind_share: float |
|
| 215 | Share of considered application in industry demand |
|
| 216 | """ |
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| 217 | ||
| 218 | # import load data |
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| 219 | ||
| 220 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 221 | "ind_osm_loadcurves" |
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| 222 | ] |
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| 223 | ||
| 224 | dsm = db.select_dataframe( |
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| 225 | f""" |
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| 226 | SELECT bus, scn_name, p_set FROM |
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| 227 | {sources['schema']}.{sources['table']} |
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| 228 | """ |
|
| 229 | ) |
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| 230 | ||
| 231 | # calculate share of timeseries for cooling and ventilation out of |
|
| 232 | # industry-data |
|
| 233 | ||
| 234 | timeseries = dsm["p_set"].copy() |
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| 235 | ||
| 236 | for index, liste in timeseries.items(): |
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| 237 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 238 | ||
| 239 | timeseries.loc[index] = share |
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| 240 | ||
| 241 | dsm["p_set"] = timeseries.copy() |
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| 242 | ||
| 243 | return dsm |
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| 244 | ||
