Skip to content

PREDISPATCHSCENARIODEMANDTRK

Tracks the predispatch 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: 1998/12/13 00:00:00

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

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

AUTHORISEDBY

The user that authorised the scenario update

SQL Type: VARCHAR(15) Polars Type: str

This column is not mandatory.

Examples:

  • MMS
  • TOM

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: 2008/05/15 09:45:27

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: 2008/05/15 09:45:27

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

# 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("AUTHORISEDDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("EFFECTIVEDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "EFFECTIVEDATE": pl.Date(),
            "VERSIONNO": int,  # or pl.UInt16
            "AUTHORISEDBY": str,
        }
    )
    # Deduplicate
    # We want one row per ['EFFECTIVEDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["EFFECTIVEDATE", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PREDISPATCHSCENARIODEMANDTRK_FILE01_202609010000.zip

EFFECTIVEDATEVERSIONNOAUTHORISEDBYAUTHORISEDDATELASTCHANGED
2005/05/16 00:00:001MMS2008/05/15 09:45:272008/05/15 09:45:27
1998/12/13 00:00:001MMS1998/12/13 00:00:002008/05/15 17:58:46
2000/04/05 00:00:001MMS2000/04/05 00:00:002008/05/15 17:58:46
2008/07/01 00:00:001TOM2008/06/18 00:00:002008/06/18 15:55:00

CSETP.WORLDDVD_PREDISPATCHSCENARIODEMANDTRKAEMOPUBLIC2026/10/0916:28:01001791527281140MONTHLY_ARCHIVE001791527281140
IPREDISPATCHSCENARIO_DEMAND_TRK1EFFECTIVEDATEVERSIONNOAUTHORISEDBYAUTHORISEDDATELASTCHANGED
DPREDISPATCHSCENARIO_DEMAND_TRK12005/05/16 00:00:001MMS2008/05/15 09:45:272008/05/15 09:45:27
DPREDISPATCHSCENARIO_DEMAND_TRK11998/12/13 00:00:001MMS1998/12/13 00:00:002008/05/15 17:58:46
DPREDISPATCHSCENARIO_DEMAND_TRK12000/04/05 00:00:001MMS2000/04/05 00:00:002008/05/15 17:58:46
DPREDISPATCHSCENARIO_DEMAND_TRK12008/07/01 00:00:001TOM2008/06/18 00:00:002008/06/18 15:55:00
CEND OF REPORT7

EFFECTIVEDATE,VERSIONNO,AUTHORISEDBY,AUTHORISEDDATE,LASTCHANGED
"2005/05/16 00:00:00",1,MMS,"2008/05/15 09:45:27","2008/05/15 09:45:27"
"1998/12/13 00:00:00",1,MMS,"1998/12/13 00:00:00","2008/05/15 17:58:46"
"2000/04/05 00:00:00",1,MMS,"2000/04/05 00:00:00","2008/05/15 17:58:46"
"2008/07/01 00:00:00",1,TOM,"2008/06/18 00:00:00","2008/06/18 15:55:00"

C,SETP.WORLD,DVD_PREDISPATCHSCENARIODEMANDTRK,AEMO,PUBLIC,2026/10/09,16:28:01,001791527281140,MONTHLY_ARCHIVE,001791527281140
I,PREDISPATCH,SCENARIO_DEMAND_TRK,1,EFFECTIVEDATE,VERSIONNO,AUTHORISEDBY,AUTHORISEDDATE,LASTCHANGED
D,PREDISPATCH,SCENARIO_DEMAND_TRK,1,"2005/05/16 00:00:00",1,MMS,"2008/05/15 09:45:27","2008/05/15 09:45:27"
D,PREDISPATCH,SCENARIO_DEMAND_TRK,1,"1998/12/13 00:00:00",1,MMS,"1998/12/13 00:00:00","2008/05/15 17:58:46"
D,PREDISPATCH,SCENARIO_DEMAND_TRK,1,"2000/04/05 00:00:00",1,MMS,"2000/04/05 00:00:00","2008/05/15 17:58:46"
D,PREDISPATCH,SCENARIO_DEMAND_TRK,1,"2008/07/01 00:00:00",1,TOM,"2008/06/18 00:00:00","2008/06/18 15:55:00"
C,"END OF REPORT",7

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 6 KB when zipped, 62 KB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

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
PREDISPATCH SCENARIO_DEMAND_TRK 1

This means that the CSV files start with metadata columns:

I,PREDISPATCH,SCENARIO_DEMAND_TRK,1,...
D,PREDISPATCH,SCENARIO_DEMAND_TRK,1,...

ROW_FILTER_TYPE is LASTCHANGED

Indexes

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