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P5MIN_SCENARIODEMAND

The P5Min scenario MW offsets

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.

REGIONID

The region to which to apply the deltaMW for this SCENARIO

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

  • QLD1

  • VIC1

  • TAS1

  • NSW1

  • SA1

  • SNOWY1

This column is mandatory, and a primary key.

SCENARIO

The scenario identifier

SQL Type: NUMBER(2,0) Polars Type: pl.Int8

This column is mandatory, and a primary key.

Examples: 1, 15

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

DELTAMW

The MW offset to apply to region total demand for this SCENARIO

SQL Type: NUMBER(4,0) Polars Type: pl.Int16

This column is not mandatory.

Examples: -50, 50, -200, 350

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").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "EFFECTIVEDATE": pl.Date(),
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SCENARIO": int,  # or pl.Int8
            "DELTAMW": int,  # or pl.Int16
        }
    )
    # Deduplicate
    # We want one row per ['EFFECTIVEDATE', 'REGIONID', 'SCENARIO', 'VERSION_DATETIME']
    .unique(
        subset=["EFFECTIVEDATE", "REGIONID", "SCENARIO", "VERSION_DATETIME"], keep="any"
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_P5MIN_SCENARIODEMAND_FILE01_202609010000.zip

EFFECTIVEDATEVERSION_DATETIMESCENARIOREGIONIDDELTAMW
2020/07/01 00:00:002020/06/03 16:15:001NSW1200
2020/07/01 00:00:002020/06/03 16:15:002NSW1-200
2020/07/01 00:00:002020/06/03 16:15:003NSW1350
2020/07/01 00:00:002020/06/03 16:15:004VIC1150
2020/07/01 00:00:002020/06/03 16:15:005VIC1-150
2020/07/01 00:00:002020/06/03 16:15:006VIC1300
2020/07/01 00:00:002020/06/03 16:15:007SA1100
2020/07/01 00:00:002020/06/03 16:15:008SA1-100
2020/07/01 00:00:002020/06/03 16:15:009SA1200
2020/07/01 00:00:002020/06/03 16:15:0010QLD1150
2020/07/01 00:00:002020/06/03 16:15:0011QLD1-150
...

CSETP.WORLDDVD_P5MIN_SCENARIODEMANDAEMOPUBLIC2026/10/0916:21:26001791526886345MONTHLY_ARCHIVE001791526886345
IP5MINSCENARIODEMAND1EFFECTIVEDATEVERSION_DATETIMESCENARIOREGIONIDDELTAMW
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:001NSW1200
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:002NSW1-200
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:003NSW1350
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:004VIC1150
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:005VIC1-150
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:006VIC1300
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:007SA1100
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:008SA1-100
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:009SA1200
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:0010QLD1150
DP5MINSCENARIODEMAND12020/07/01 00:00:002020/06/03 16:15:0011QLD1-150
...
CEND OF REPORT18

EFFECTIVEDATE,VERSION_DATETIME,SCENARIO,REGIONID,DELTAMW
"2020/07/01 00:00:00","2020/06/03 16:15:00",1,NSW1,200
"2020/07/01 00:00:00","2020/06/03 16:15:00",2,NSW1,-200
"2020/07/01 00:00:00","2020/06/03 16:15:00",3,NSW1,350
"2020/07/01 00:00:00","2020/06/03 16:15:00",4,VIC1,150
"2020/07/01 00:00:00","2020/06/03 16:15:00",5,VIC1,-150
"2020/07/01 00:00:00","2020/06/03 16:15:00",6,VIC1,300
"2020/07/01 00:00:00","2020/06/03 16:15:00",7,SA1,100
"2020/07/01 00:00:00","2020/06/03 16:15:00",8,SA1,-100
"2020/07/01 00:00:00","2020/06/03 16:15:00",9,SA1,200
"2020/07/01 00:00:00","2020/06/03 16:15:00",10,QLD1,150
"2020/07/01 00:00:00","2020/06/03 16:15:00",11,QLD1,-150
...

C,SETP.WORLD,DVD_P5MIN_SCENARIODEMAND,AEMO,PUBLIC,2026/10/09,16:21:26,001791526886345,MONTHLY_ARCHIVE,001791526886345
I,P5MIN,SCENARIODEMAND,1,EFFECTIVEDATE,VERSION_DATETIME,SCENARIO,REGIONID,DELTAMW
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",1,NSW1,200
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",2,NSW1,-200
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",3,NSW1,350
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",4,VIC1,150
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",5,VIC1,-150
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",6,VIC1,300
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",7,SA1,100
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",8,SA1,-100
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",9,SA1,200
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",10,QLD1,150
D,P5MIN,SCENARIODEMAND,1,"2020/07/01 00:00:00","2020/06/03 16:15:00",11,QLD1,-150
...
C,"END OF REPORT",18

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 6 KB when zipped, 61 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "insert", which means that you will usually not see duplicate data. If you do, you can deduplicate based on the primary keys, without sorting first. Rows from different files with the same primary keys should have the same values.

PDR Loader configuration is:

Report Name Report Subtype Version
P5MIN SCENARIODEMAND 1

This means that the CSV files start with metadata columns:

I,P5MIN,SCENARIODEMAND,1,...
D,P5MIN,SCENARIODEMAND,1,...

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