P5MIN_SCENARIODEMANDTRK

Tracks the 5Min scenario offset updates across time

Column Schema

EFFECTIVEDATE

The effective date of this set of scenarios

SQL Type: DATE Polars Type: pl.Date()

This column is mandatory, and a primary key.

Example: 2020/07/01 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

VERSION_DATETIME

The version of this set of scenarios

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2020/06/03 16:15:00

AUTHORISEDDATE

The datetime that the scenario update was authorised

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2020/06/03 16:15:00

LASTCHANGED

The datetime that the record was last changed

For more information, see the dedicated documentation page.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2021/07/07 09:21:44

New Data Timing

On update of P5MIN scenarios

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

# Dates are represented as datetimes at midnight

d_fmt = "%Y/%m/%d 00:00:00"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .with_columns(
        pl.col("VERSION_DATETIME", "AUTHORISEDDATE", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
        pl.col("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast({"EFFECTIVEDATE": pl.Date()})
    # Deduplicate
    # We want one row per ['EFFECTIVEDATE', 'VERSION_DATETIME']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["EFFECTIVEDATE", "VERSION_DATETIME"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_P5MIN_SCENARIODEMANDTRK_FILE01_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

EFFECTIVEDATE	VERSION_DATETIME	AUTHORISEDDATE	LASTCHANGED
2020/07/01 00:00:00	2020/06/03 16:15:00	2020/06/03 16:15:00	2021/07/07 09:21:44

C	SETP.WORLD	DVD_P5MIN_SCENARIODEMANDTRK	AEMO	PUBLIC	2026/10/09	16:21:27	001791526887242	MONTHLY_ARCHIVE	001791526887242
I	P5MIN	SCENARIODEMANDTRK	1	EFFECTIVEDATE	VERSION_DATETIME	AUTHORISEDDATE	LASTCHANGED
D	P5MIN	SCENARIODEMANDTRK	1	2020/07/01 00:00:00	2020/06/03 16:15:00	2020/06/03 16:15:00	2021/07/07 09:21:44
C	END OF REPORT	4

EFFECTIVEDATE,VERSION_DATETIME,AUTHORISEDDATE,LASTCHANGED
"2020/07/01 00:00:00","2020/06/03 16:15:00","2020/06/03 16:15:00","2021/07/07 09:21:44"

Raw Text with Metadata

C,SETP.WORLD,DVD_P5MIN_SCENARIODEMANDTRK,AEMO,PUBLIC,2026/10/09,16:21:27,001791526887242,MONTHLY_ARCHIVE,001791526887242
I,P5MIN,SCENARIODEMANDTRK,1,EFFECTIVEDATE,VERSION_DATETIME,AUTHORISEDDATE,LASTCHANGED
D,P5MIN,SCENARIODEMANDTRK,1,"2020/07/01 00:00:00","2020/06/03 16:15:00","2020/06/03 16:15:00","2021/07/07 09:21:44"
C,"END OF REPORT",4

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 6 of 2021. The most recent available data is month 9 of 2026.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 5 KB when zipped, 53 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

• EFFECTIVEDATE

• VERSION_DATETIME

The transaction type is "INSERT-UPDATE", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name	Report Subtype	Version
P5MIN	SCENARIODEMANDTRK	1

This means that the CSV files start with metadata columns:

I,P5MIN,SCENARIODEMANDTRK,1,...
D,P5MIN,SCENARIODEMANDTRK,1,...

ROW_FILTER_TYPE is LASTCHANGED

Links

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: P5MIN

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