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Fixed Residual Consumption Data

Total number of rows: 4,272,982


2019-09-05 21:00Z 2019-09-05 23:00 911 None
2019-09-05 21:00Z 2019-09-05 23:00 860 None
2019-09-05 21:00Z 2019-09-05 23:00 856 None
2019-09-05 21:00Z 2019-09-05 23:00 854 None
2019-09-05 21:00Z 2019-09-05 23:00 853 None
2019-09-05 21:00Z 2019-09-05 23:00 791 None
2019-09-05 21:00Z 2019-09-05 23:00 757 None
2019-09-05 21:00Z 2019-09-05 23:00 755 None
2019-09-05 21:00Z 2019-09-05 23:00 740 None
2019-09-05 21:00Z 2019-09-05 23:00 592 None
2019-09-05 21:00Z 2019-09-05 23:00 591 None
2019-09-05 21:00Z 2019-09-05 23:00 590 None
2019-09-05 21:00Z 2019-09-05 23:00 589 None
2019-09-05 21:00Z 2019-09-05 23:00 588 None
2019-09-05 21:00Z 2019-09-05 23:00 587 None
2019-09-05 21:00Z 2019-09-05 23:00 584 None
2019-09-05 21:00Z 2019-09-05 23:00 554 None
2019-09-05 21:00Z 2019-09-05 23:00 553 None
2019-09-05 21:00Z 2019-09-05 23:00 552 None
2019-09-05 21:00Z 2019-09-05 23:00 543 None
2019-09-05 21:00Z 2019-09-05 23:00 533 None
2019-09-05 21:00Z 2019-09-05 23:00 532 None
2019-09-05 21:00Z 2019-09-05 23:00 531 None
2019-09-05 21:00Z 2019-09-05 23:00 443 None
2019-09-05 21:00Z 2019-09-05 23:00 398 None
2019-09-05 21:00Z 2019-09-05 23:00 397 None
2019-09-05 21:00Z 2019-09-05 23:00 396 None
2019-09-05 21:00Z 2019-09-05 23:00 394 None
2019-09-05 21:00Z 2019-09-05 23:00 392 None
2019-09-05 21:00Z 2019-09-05 23:00 385 None
2019-09-05 21:00Z 2019-09-05 23:00 384 None
2019-09-05 21:00Z 2019-09-05 23:00 381 None
2019-09-05 21:00Z 2019-09-05 23:00 371 None
2019-09-05 21:00Z 2019-09-05 23:00 370 None
2019-09-05 21:00Z 2019-09-05 23:00 359 None
2019-09-05 21:00Z 2019-09-05 23:00 357 None
2019-09-05 21:00Z 2019-09-05 23:00 353 None
2019-09-05 21:00Z 2019-09-05 23:00 351 None
2019-09-05 21:00Z 2019-09-05 23:00 348 None
2019-09-05 21:00Z 2019-09-05 23:00 347 None
2019-09-05 21:00Z 2019-09-05 23:00 344 None
2019-09-05 21:00Z 2019-09-05 23:00 342 None
2019-09-05 21:00Z 2019-09-05 23:00 341 None
2019-09-05 21:00Z 2019-09-05 23:00 331 None
2019-09-05 21:00Z 2019-09-05 23:00 248 None
2019-09-05 21:00Z 2019-09-05 23:00 246 None
2019-09-05 21:00Z 2019-09-05 23:00 245 None
2019-09-05 21:00Z 2019-09-05 23:00 244 None
2019-09-05 21:00Z 2019-09-05 23:00 233 None
2019-09-05 21:00Z 2019-09-05 23:00 232 None

Data Dictionary

Column Type Label Description
HourUTC timestamptz
HourDK timestamp
GridCompany text
ResidualConsumption float8

Hour UTC

Name Hour UTC
Description

A date and time (interval), shown in UTC time zone, where the values are valid. 00:00 o’clock is the first hour of a given day interval 00:00 - 00:59 and 01:00 covers the second hour (interval) of the day and so forth. Please note: The naming is based on the length of the interval of the finest grain of the resolution.

Comment

Please note that the format shown in the example applies to data download as JSON, XML or fetched through the API and is in accordance with the ISO 8601 standard. The format is slightly different when it is shown on screen or downloaded manually as CSV or XLSX. This is mainly due to readability and consideration for Excel users.

In preview (in the GUI) all timestamps are shown as (display convention) YYYY-MM-DD hh:mmZ e.g. 2017-07-14 08:00Z. The Z will remind viewers that this is UTC.

In download (CSV and XLSX) the date time are exported as YYYY-MM-DD hh:mm e.g. 2017-07-14 08:00. That is without the “T” and the “Z” and the seconds. Excel will recognize it as date-time. The user must remember the convention about time zones.

In download (JSON and XML) the full format is used YYYY-MM-DDThh:mmZ e.g. 2017-07-14T08:00Z.

Example 2017-07-14T08:00Z
Unit
Field HourUTC
Property Contstraint
Type timestamptz
Size 17
Format Regex
Validation Rules Always full hours, i.e. minutes are 00

Hour DK

Name Hour DK
Description

A date and time (interval), shown in Danish time zone, where the values are valid. 00:00 o’clock is the first hour of a given day, interval 00:00 - 00:59, and 01:00 covers the second hour period (interval) of the day and so forth.

Comment

On one normal day there will be 24 intervals.

When daylight saving times shifts there will be either 23 or 25 intervals.

Please note that the format shown in the example applies to data download as JSON, XML or fetched through the API and is in accordance with the ISO 8601 standard. The format is slightly different when it is shown on screen or downloaded manually as CSV or XLSX. This is mainly due to readability and consideration for Excel users.

In preview (in the GUI) all timestamps are shown as (display convention) YYYY-MM-DD hh:mm e.g. 2017-07-14 08:00. Please note that is no time zone indicator, showning that this is local (Danish) time.

In download (CSV and XLSX) the date time are exported as YYYY-MM-DD hh:mm e.g. 2017-07-14 08:00. That is without the “T” and the seconds. Excel will recognize it as date-time. The user must remember that this is local (Danish) time.

In download (JSON and XML) the format used is YYYY-MM-DDThh:mm e.g. 2017-07-14T08:00.

Example 2017-07-14T08:00
Unit
Field HourDK
Property Contstraint
Type timestamp
Size 17
Format Regex
Validation Rules Always full hours, i.e. minutes are 00

Grid company

Name Grid company
Description

Grid Company number according Danish Energy Association

Comment
Example 031
Unit text
Field GridCompany
Property Contstraint
Type text
Size 3
Format Regex
Validation Rules >000 and <= 999

Residual consumption

Name Residual consumption
Description

The residual consumption is calculated hour by hour and consists of the total consumption of the individual grid area deducted the consumption of all remote meter reading customers.

Comment

00:00 o’clock is the first hour of a given day interval 00:00 - 00:59 and 01:00 covers the second hour (interval) of the day and so forth. Please note: The naming is based on the length of the interval of the finest grain of the resolution

Example 300,00
Unit MWh per hour
Field ResidualConsumption
Property Contstraint
Type float
Size 9.1
Format Regex ([0-9]*[,])[0-9]
Validation Rules >=0