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PREDISPATCHSCENARIODEMAND

PREDISPATCHSCENARIODEMAND defines the demand offsets that are applied for each of the predispatch sensitivity scenarios.

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: 1998/12/13 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 VARCHAR(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) Polars Type: pl.UInt8

This column is mandatory, and a primary key.

Examples: 1, 43

VERSIONNO

The version of this set of scenarios

SQL Type: NUMBER(3) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

Example: 1

DELTAMW

The MW offset that is applied for this scenario

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

This column is not mandatory.

Examples: 0, -50, 50, -500, 1000

New Data Timing

When the predispatch scenario offset is updated

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

# Dates are represented as datetimes at midnight

# e.g. "2029/03/15 00:00:00"

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("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "EFFECTIVEDATE": pl.Date(),
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SCENARIO": int,  # or pl.UInt8
            "VERSIONNO": int,  # or pl.UInt16
            "DELTAMW": int,  # or pl.Int16
        }
    )
    # Deduplicate
    # We want one row per ['EFFECTIVEDATE', 'REGIONID', 'SCENARIO', 'VERSIONNO']
    .unique(subset=["EFFECTIVEDATE", "REGIONID", "SCENARIO", "VERSIONNO"], keep="any")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PREDISPATCHSCENARIODEMAND_FILE01_202609010000.zip

EFFECTIVEDATEVERSIONNOSCENARIOREGIONIDDELTAMW
2005/05/16 00:00:0011TAS10
2005/05/16 00:00:0011SNOWY10
2005/05/16 00:00:0011SA10
2005/05/16 00:00:0011VIC10
2005/05/16 00:00:0011QLD10
2005/05/16 00:00:0011NSW1100
2005/05/16 00:00:0012SNOWY10
2005/05/16 00:00:0012SA10
2005/05/16 00:00:0012TAS10
2005/05/16 00:00:0012VIC10
2005/05/16 00:00:0012QLD10
...

CSETP.WORLDDVD_PREDISPATCHSCENARIODEMANDAEMOPUBLIC2026/10/0916:28:00001791527280707MONTHLY_ARCHIVE001791527280707
IPREDISPATCHSCENARIO_DEMAND1EFFECTIVEDATEVERSIONNOSCENARIOREGIONIDDELTAMW
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0011TAS10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0011SNOWY10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0011SA10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0011VIC10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0011QLD10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0011NSW1100
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0012SNOWY10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0012SA10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0012TAS10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0012VIC10
DPREDISPATCHSCENARIO_DEMAND12005/05/16 00:00:0012QLD10
...
CEND OF REPORT386

EFFECTIVEDATE,VERSIONNO,SCENARIO,REGIONID,DELTAMW
"2005/05/16 00:00:00",1,1,TAS1,0
"2005/05/16 00:00:00",1,1,SNOWY1,0
"2005/05/16 00:00:00",1,1,SA1,0
"2005/05/16 00:00:00",1,1,VIC1,0
"2005/05/16 00:00:00",1,1,QLD1,0
"2005/05/16 00:00:00",1,1,NSW1,100
"2005/05/16 00:00:00",1,2,SNOWY1,0
"2005/05/16 00:00:00",1,2,SA1,0
"2005/05/16 00:00:00",1,2,TAS1,0
"2005/05/16 00:00:00",1,2,VIC1,0
"2005/05/16 00:00:00",1,2,QLD1,0
...

C,SETP.WORLD,DVD_PREDISPATCHSCENARIODEMAND,AEMO,PUBLIC,2026/10/09,16:28:00,001791527280707,MONTHLY_ARCHIVE,001791527280707
I,PREDISPATCH,SCENARIO_DEMAND,1,EFFECTIVEDATE,VERSIONNO,SCENARIO,REGIONID,DELTAMW
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,1,TAS1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,1,SNOWY1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,1,SA1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,1,VIC1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,1,QLD1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,1,NSW1,100
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,2,SNOWY1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,2,SA1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,2,TAS1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,2,VIC1,0
D,PREDISPATCH,SCENARIO_DEMAND,1,"2005/05/16 00:00:00",1,2,QLD1,0
...
C,"END OF REPORT",386

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 1 of 2015. 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 23 KB when zipped, 229 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
PREDISPATCH SCENARIO_DEMAND 1

This means that the CSV files start with metadata columns:

I,PREDISPATCH,SCENARIO_DEMAND,1,...
D,PREDISPATCH,SCENARIO_DEMAND,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: PRE_DISPATCH