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PRUDENTIALRUNTRK

Records the prudential run accepted by Settlements staff for each prudential date

Column Schema

PRUDENTIAL_DATE

The prudential date

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

This column is mandatory, and a primary key.

Example: 2026/09/02 00:00:00

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

RUNNO

The run number for the prudential date

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

This column is mandatory, and a primary key.

Examples: 1, 26

AUTHORISEDBY

The user that authorised the prudential run

SQL Type: VARCHAR(15) Polars Type: str

This column is not mandatory.

Examples:

  • JHUANG2
  • JKOUNIS
  • TOYONG

AUTHORISEDDATE

The datetime that the prudential run 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: 2026/09/02 09:36:28

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: 2026/09/02 01:26:15

New Data Timing

Each prudential run

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

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PRUDENTIALRUNTRK_FILE01_202609010000.zip

PRUDENTIAL_DATERUNNOAUTHORISEDBYAUTHORISEDDATELASTCHANGED
2026/09/02 00:00:0012026/09/02 01:26:15
2026/09/02 00:00:002JKOUNIS2026/09/02 09:36:282026/09/02 09:36:28
2026/09/02 00:00:0032026/09/02 09:36:45
2026/09/02 00:00:0042026/09/02 09:37:35
2026/09/02 00:00:0052026/09/02 11:00:45
2026/09/02 00:00:0062026/09/02 11:05:35
2026/09/02 00:00:0072026/09/02 11:06:30
2026/09/02 00:00:0082026/09/02 11:09:10
2026/09/02 00:00:0092026/09/02 11:09:40
2026/09/02 00:00:00102026/09/02 11:18:00
2026/09/02 00:00:00112026/09/02 11:18:40
...

CSETP.WORLDDVD_PRUDENTIALRUNTRKAEMOPUBLIC2026/10/0916:28:01001791527281281MONTHLY_ARCHIVE001791527281281
IPRUDENTIALRUNTRK1PRUDENTIAL_DATERUNNOAUTHORISEDBYAUTHORISEDDATELASTCHANGED
DPRUDENTIALRUNTRK12026/09/02 00:00:0012026/09/02 01:26:15
DPRUDENTIALRUNTRK12026/09/02 00:00:002JKOUNIS2026/09/02 09:36:282026/09/02 09:36:28
DPRUDENTIALRUNTRK12026/09/02 00:00:0032026/09/02 09:36:45
DPRUDENTIALRUNTRK12026/09/02 00:00:0042026/09/02 09:37:35
DPRUDENTIALRUNTRK12026/09/02 00:00:0052026/09/02 11:00:45
DPRUDENTIALRUNTRK12026/09/02 00:00:0062026/09/02 11:05:35
DPRUDENTIALRUNTRK12026/09/02 00:00:0072026/09/02 11:06:30
DPRUDENTIALRUNTRK12026/09/02 00:00:0082026/09/02 11:09:10
DPRUDENTIALRUNTRK12026/09/02 00:00:0092026/09/02 11:09:40
DPRUDENTIALRUNTRK12026/09/02 00:00:00102026/09/02 11:18:00
DPRUDENTIALRUNTRK12026/09/02 00:00:00112026/09/02 11:18:40
...
CEND OF REPORT314

PRUDENTIAL_DATE,RUNNO,AUTHORISEDBY,AUTHORISEDDATE,LASTCHANGED
"2026/09/02 00:00:00",1,,,"2026/09/02 01:26:15"
"2026/09/02 00:00:00",2,JKOUNIS,"2026/09/02 09:36:28","2026/09/02 09:36:28"
"2026/09/02 00:00:00",3,,,"2026/09/02 09:36:45"
"2026/09/02 00:00:00",4,,,"2026/09/02 09:37:35"
"2026/09/02 00:00:00",5,,,"2026/09/02 11:00:45"
"2026/09/02 00:00:00",6,,,"2026/09/02 11:05:35"
"2026/09/02 00:00:00",7,,,"2026/09/02 11:06:30"
"2026/09/02 00:00:00",8,,,"2026/09/02 11:09:10"
"2026/09/02 00:00:00",9,,,"2026/09/02 11:09:40"
"2026/09/02 00:00:00",10,,,"2026/09/02 11:18:00"
"2026/09/02 00:00:00",11,,,"2026/09/02 11:18:40"
...

C,SETP.WORLD,DVD_PRUDENTIALRUNTRK,AEMO,PUBLIC,2026/10/09,16:28:01,001791527281281,MONTHLY_ARCHIVE,001791527281281
I,PRUDENTIAL,RUNTRK,1,PRUDENTIAL_DATE,RUNNO,AUTHORISEDBY,AUTHORISEDDATE,LASTCHANGED
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",1,,,"2026/09/02 01:26:15"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",2,JKOUNIS,"2026/09/02 09:36:28","2026/09/02 09:36:28"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",3,,,"2026/09/02 09:36:45"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",4,,,"2026/09/02 09:37:35"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",5,,,"2026/09/02 11:00:45"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",6,,,"2026/09/02 11:05:35"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",7,,,"2026/09/02 11:06:30"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",8,,,"2026/09/02 11:09:10"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",9,,,"2026/09/02 11:09:40"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",10,,,"2026/09/02 11:18:00"
D,PRUDENTIAL,RUNTRK,1,"2026/09/02 00:00:00",11,,,"2026/09/02 11:18:40"
...
C,"END OF REPORT",314

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.

Warning

There are some months with no files.

Data Size

Each year of data is approximately 36 KB when zipped, 364 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
PRUDENTIAL RUNTRK 1

This means that the CSV files start with metadata columns:

I,PRUDENTIAL,RUNTRK,1,...
D,PRUDENTIAL,RUNTRK,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: PRUDENTIALS