openego /
eGon-data
| @@ 238-272 (lines=35) @@ | ||
| 235 | return dsm |
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| 236 | ||
| 237 | ||
| 238 | def ind_osm_data_import_individual(ind_vent_cool_share): |
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| 239 | """ |
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| 240 | Import industry data per osm-area necessary to identify DSM-potential. |
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| 241 | ---------- |
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| 242 | ind_share: float |
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| 243 | Share of considered application in industry demand |
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| 244 | """ |
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| 245 | ||
| 246 | # import load data |
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| 247 | ||
| 248 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 249 | "ind_osm_loadcurves_individual" |
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| 250 | ] |
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| 251 | ||
| 252 | dsm = db.select_dataframe( |
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| 253 | f""" |
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| 254 | SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
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| 255 | {sources['schema']}.{sources['table']} |
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| 256 | """ |
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| 257 | ) |
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| 258 | ||
| 259 | # calculate share of timeseries for cooling and ventilation out of |
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| 260 | # industry-data |
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| 261 | ||
| 262 | timeseries = dsm["p_set"].copy() |
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| 263 | ||
| 264 | for index, liste in timeseries.items(): |
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| 265 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 266 | ||
| 267 | timeseries.loc[index] = share |
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| 268 | ||
| 269 | dsm["p_set"] = timeseries.copy() |
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| 270 | ||
| 271 | return dsm |
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| 272 | ||
| 273 | ||
| 274 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 275 | """ |
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| @@ 202-233 (lines=32) @@ | ||
| 199 | return dsm |
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| 200 | ||
| 201 | ||
| 202 | def ind_osm_data_import(ind_vent_cool_share): |
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| 203 | """ |
|
| 204 | Import industry data per osm-area necessary to identify DSM-potential. |
|
| 205 | ---------- |
|
| 206 | ind_share: float |
|
| 207 | Share of considered application in industry demand |
|
| 208 | """ |
|
| 209 | ||
| 210 | # import load data |
|
| 211 | ||
| 212 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 213 | "ind_osm_loadcurves" |
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| 214 | ] |
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| 215 | ||
| 216 | dsm = db.select_dataframe( |
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| 217 | f""" |
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| 218 | SELECT bus, scn_name, p_set FROM |
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| 219 | {sources['schema']}.{sources['table']} |
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| 220 | """ |
|
| 221 | ) |
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| 222 | ||
| 223 | # calculate share of timeseries for cooling and ventilation out of |
|
| 224 | # industry-data |
|
| 225 | ||
| 226 | timeseries = dsm["p_set"].copy() |
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| 227 | ||
| 228 | for index, liste in timeseries.items(): |
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| 229 | share = [float(item) * ind_vent_cool_share for item in liste] |
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| 230 | ||
| 231 | timeseries.loc[index] = share |
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| 232 | ||
| 233 | dsm["p_set"] = timeseries.copy() |
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| 234 | ||
| 235 | return dsm |
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| 236 | ||
