You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
As an example, this scenario dataset has meta information and in the 'resources' -> 'fileds' there are some descriptions about the columns. However, typically, these descriptions are not sufficient for inferring an OEO concept for the column.
As of 'May 2nd, 2022', there are 238 unique column names in total (all uploaded scenario datasets). Some of them can be mapped easily to the OEO concepts:
#
column name
OEO concept
OEO class ID
1
id
unique individual identifier
OEO_00010037
2
scenario
scenario
OEO_00000364
3
constr
constraint
OEO_00000104
4
val
5
region
spatial region
BFO_0000006
6
sector
sector
OEO_00000367
7
type
8
demand
demand
OEO_00140040
9
energy
energy
OEO_00000150
10
reg
11
nutsid
12
geom
13
technology
technology
OEO_00000407
14
co2_var
15
co2_fix
16
eta_elec
17
eta_th
18
eta_el_chp
19
eta_th_chp
20
eta_chp_flex_el
21
sigma_chp
22
beta_chp
23
opex_var
24
opex_fix
25
capex
26
c_rate_in
27
c_rate_out
28
eta_in
29
eta_out
30
cap_loss
31
lifetime
32
wacc
33
ressource
34
transformer
transformer
OEO_00000420
35
power
power
OEO_00000333
36
from_region
37
to_region
38
capacity
39
parameter
40
value
41
unit
unit of measurement
UO_0000000
42
country_code
43
category
44
year
year
UO_0000036
45
gas
46
notation
47
ry
48
base_year
49
default_unit
50
additional_unit
51
source_original
52
comment
53
value_reported
54
value_ry_calibration
55
value_gapfilled
56
submission_year_original
57
subtable
58
data_source_years
59
data_source
60
is_part_of_projections
61
is_ry
62
category_code
63
category_lulucf
64
category_parent
65
level
66
is_user_defined
67
in_cir_2020_1208_annex_xxv_art_38_tab_1a
68
in_cir_749_2014_annex_xii_art_23_tab_1
69
crf_code
70
oeo_id
71
country_name
72
notation_name
73
detail
74
scenario_name
75
energy_carrier
energy carrier
OEO_00020039
76
avg_value
77
hourly_value
78
Szenario
scenario
OEO_00000364
79
Szenariojahr
scenario year
OEO_00020097
80
Region
spatial region
BFO_0000006
81
Kennwert
82
Unterkennwert
83
Sektor
sector
OEO_00000367
84
Energieträger
85
Technologie
technology
OEO_00000407
86
Einheit
87
Wert
88
Kommentar
89
Zeit
90
Bivalente_Luftwärmepumpe
91
Hybride_Luftwärmepumpe
92
Sondenwärmepumpe
93
Ländername
94
Erzeugung_Braunkohle_Kraft_Waerme_Kopplung
95
Erzeugung_Steinkohle_Kraft_Waerme_Kopplung
96
Erzeugung_Erdgas_Kraft_Waerme_Kopplung
97
Erzeugung_Öl_Kraft_Waerme_Kopplung
98
Erzeugung_Braunkohle_Kondensationskraftwerke
99
Erzeugung_Steinkohle_Kondensationskraftwerke
100
Erzeugung_Erdgas_Kondensationskraftwerke
101
Erzeugung_Öl_Kondensationskraftwerke
102
Erzeugung_Uran_Kondensationskraftwerke
103
Erzeugung_Wind_Onshore
104
Erzeugung_Wind_Offshore
105
Erzeugung_Photovoltaik
106
Erzeugung_Laufwasser
107
Erzeugung_Pumpspeicher
108
Erzeugung_Batterien
109
Verbrauch_Herkoemmlich_inkl_Klimatisierung
110
Verbrauch_Pumpspeicher
111
Verbrauch_Batterien
112
Verbrauch_Wärmepumpen
113
Verbrauch_Heizstäbe
114
Verbrauch_Power_to_Gas
115
Verbrauch_Batterie_elektrische_Fahrzeuge
116
Verbrauch_Plug_in_Hybride
117
Verbrauch_Range_Extender
118
Import
119
Export
120
Abregelung_Wind_Onshore
121
Abregelung_Wind_Offshore
122
Abregelung_Photovoltaik
123
variable
variable
OEO_00000435
124
source_category
125
crf
126
application
127
target_fulfilled
128
fuel
fuel
OEO_00000173
129
subsector
130
emission_source
131
greenhouse_gas
greenhouse gas
OEO_00000020
132
technologie
133
jahr
year
UO_0000036
134
at
135
be
136
ch
137
cz
138
dk
139
fr
140
gb
141
lu
142
nl
143
no
144
pl
145
se
146
bundesland
147
szenario
scenario
OEO_00000364
148
braunkohle
149
erdgas
150
kuppelgas
151
oel
152
abfall
153
sonstige_konventionelle
154
kwk_kleiner_10mw
155
pumpspeicher
156
lauf_und_wasserspeicher
157
wind_onshore
onshore wind farm
OEO_00000311
158
wind_offshore
offshore wind farm
OEO_00000308
159
photovoltaik
160
biomasse
biomass
OEO_00010214
161
sonstige_ee
162
band_des_stromverbrauchs_von
163
band_des_stromverbrauchs_bis
164
dsm
165
power_to_heat
166
power_to_gas
167
kategorie
168
energietraeger
169
referenz_2019
170
a_2035
171
b_2035
172
c_2035
173
b_2040
174
stunde
175
mittelwert
176
minimum
177
maximum
178
zeit
179
ungesteuerter_lastgang
180
optimierter_lastgang
181
konventioneller_stromverbrauch
182
elektromoblitaet
183
power_to_heat_haushalte
184
grossverbraucher
185
power_to_heat_industrie
186
sektor
sector
OEO_00000367
187
referenz_2018
188
name
189
carrier
190
tech
191
from_bus
192
to_bus
193
capacity_cost
194
efficiency
195
carrier_cost
196
marginal_cost
197
expandable
198
output_parameters
199
balanced
200
bus
201
amount
202
profile
203
timeindex
204
be_electricity_demand_profile
205
bb_electricity_demand_profile
206
storage_capacity
207
loss_rate
208
storage_capacity_cost
209
input_parameters
210
loss
211
be_solar_pv_profile
212
bb_solar_pv_profile
213
be_wind_onshore_profile
214
bb_wind_onshore_profile
215
dfid
216
nid
217
pathway
218
framework
219
version
220
region_2
221
indicator
222
aggregation
223
tags
224
updated
225
schema
226
field
227
source
228
month
month
UO_0000035
229
year_month
230
rid
231
set
232
internal_id
233
member_state
234
submission_year
235
crf_sector
236
additional_unit_information
237
notation_key
238
is_baseyear
The text was updated successfully, but these errors were encountered:
adelmemariani
changed the title
Alignment of the abbreviated terms in the datasets' columns or meta information.
Alignment of the abbreviated terms in the datasets' columns or meta information with the OEO terms.
Jun 2, 2022
adelmemariani
changed the title
Alignment of the abbreviated terms in the datasets' columns or meta information with the OEO terms.
Alignment of the abbreviated column names or meta information of the datasets with the OEO terms.
Jun 2, 2022
adelmemariani
changed the title
Alignment of the abbreviated column names or meta information of the datasets with the OEO terms.
Alignment of the abbreviated column names or meta information of the scenario datasets with the OEO terms.
Jun 2, 2022
In principle, if the institutions provide enough textual descriptions for the column names of their datasets (and also the distinct values in the categorical columns), then it is possible to automatically recommend to them some candidate OEO concepts: A recommender engine (inside the OEP) for the alignments between the datasets' columns and the OEO terms. I already made a prototype, however, the performance of the final product depends on the quality of the descriptions.
As an example, this scenario dataset has meta information and in the 'resources' -> 'fileds' there are some descriptions about the columns. However, typically, these descriptions are not sufficient for inferring an OEO concept for the column.
As of 'May 2nd, 2022', there are 238 unique column names in total (all uploaded scenario datasets). Some of them can be mapped easily to the OEO concepts:
The text was updated successfully, but these errors were encountered: