openego /
eGon-data
| @@ 206-237 (lines=32) @@ | ||
| 203 | return dsm |
|
| 204 | ||
| 205 | ||
| 206 | def ind_osm_data_import(ind_vent_cool_share): |
|
| 207 | """ |
|
| 208 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 209 | ---------- |
|
| 210 | ind_share: float |
|
| 211 | Share of considered application in industry demand |
|
| 212 | """ |
|
| 213 | ||
| 214 | # import load data |
|
| 215 | ||
| 216 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 217 | "ind_osm_loadcurves" |
|
| 218 | ] |
|
| 219 | ||
| 220 | dsm = db.select_dataframe( |
|
| 221 | f""" |
|
| 222 | SELECT bus, scn_name, p_set FROM |
|
| 223 | {sources['schema']}.{sources['table']} |
|
| 224 | """ |
|
| 225 | ) |
|
| 226 | ||
| 227 | # calculate share of timeseries for cooling and ventilation out of |
|
| 228 | # industry-data |
|
| 229 | ||
| 230 | timeseries = dsm["p_set"].copy() |
|
| 231 | ||
| 232 | for index, liste in timeseries.items(): |
|
| 233 | share = [float(item) * ind_vent_cool_share for item in liste] |
|
| 234 | ||
| 235 | timeseries.loc[index] = share |
|
| 236 | ||
| 237 | dsm["p_set"] = timeseries.copy() |
|
| 238 | ||
| 239 | return dsm |
|
| 240 | ||
| @@ 242-276 (lines=35) @@ | ||
| 239 | return dsm |
|
| 240 | ||
| 241 | ||
| 242 | def ind_osm_data_import_individual(ind_vent_cool_share): |
|
| 243 | """ |
|
| 244 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 245 | ---------- |
|
| 246 | ind_share: float |
|
| 247 | Share of considered application in industry demand |
|
| 248 | """ |
|
| 249 | ||
| 250 | # import load data |
|
| 251 | ||
| 252 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 253 | "ind_osm_loadcurves_individual" |
|
| 254 | ] |
|
| 255 | ||
| 256 | dsm = db.select_dataframe( |
|
| 257 | f""" |
|
| 258 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
|
| 259 | {sources['schema']}.{sources['table']} |
|
| 260 | """ |
|
| 261 | ) |
|
| 262 | ||
| 263 | # calculate share of timeseries for cooling and ventilation out of |
|
| 264 | # industry-data |
|
| 265 | ||
| 266 | timeseries = dsm["p_set"].copy() |
|
| 267 | ||
| 268 | for index, liste in timeseries.items(): |
|
| 269 | share = [float(item) * ind_vent_cool_share for item in liste] |
|
| 270 | ||
| 271 | timeseries.loc[index] = share |
|
| 272 | ||
| 273 | dsm["p_set"] = timeseries.copy() |
|
| 274 | ||
| 275 | return dsm |
|
| 276 | ||
| 277 | ||
| 278 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 279 | """ |
|
