METERDATATRK

METERDATATRK records meter data files submitted for each connection point on a daily basis. The same data is provided in METERDATA period by period (i.e. 48 records), whereas METERDATATRK shows one record per day for each file submitted for a connection point.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

METERDATATRK data is confidential to the relevant participant.

Source

METERDATATRK updates whenever meter files are processed.

Volume

Depends on the number of TNI, FRMP and LR combinations plus the number of data file loads (versions) from MSATS per settlement run.

Warning

This is a different table to METERDATA_TRK

Column Schema

CONNECTIONPOINTID

Transmission Node Identifier (TNI)

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

HOSTDISTRIBUTOR

Local retailer participant identifier

Warning

In the CSV files, this column actually has header LOCAL_RETAILER, not HOSTDISTRIBUTOR. In a SQL database, this should appear as HOSTDISTRIBUTOR

SQL Type: VARCHAR2(10) Polars Type: str

This column is mandatory, and a primary key.

METERINGDATAAGENT

Defaults to MSATS

SQL Type: VARCHAR2(10) Polars Type: str

This column is mandatory, and a primary key.

METERRUNNO

Meter data version number

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is mandatory, and a primary key.

PARTICIPANTID

Participant identifier

Warning

In the CSV files, this column actually has header FRMP, not PARTICIPANTID. In a SQL database, this should appear as PARTICIPANTID

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

SETTLEMENTDATE

Settlement calendar date

Warning

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

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

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

AUTHORISEDDATE

Date processed

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: 2026/01/20 00:05:00

FILENAME

Meter file name (MSATS file name)

SQL Type: VARCHAR2(40) Polars Type: str

This column is not mandatory.

LASTCHANGED

Last date and time record changed

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Example: 2026/01/20 00:05:00

ACKFILENAME

Not used

SQL Type: VARCHAR2(40) Polars Type: str

This column is unused.

AUTHORISEDBY

Not used

SQL Type: VARCHAR2(15) Polars Type: str

This column is unused.

New Data Timing

Updates whenever meter files are processed

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"

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",
    )
    # These columns have names in the CSV which do not match the official schema
    .rename({"LOCAL_RETAILER": "HOSTDISTRIBUTOR", "FRMP": "PARTICIPANTID"})
    .with_columns(
        pl.col("SETTLEMENTDATE", "AUTHORISEDDATE", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "CONNECTIONPOINTID": pl.Categorical,
            "HOSTDISTRIBUTOR": str,
            "METERINGDATAAGENT": str,
            "METERRUNNO": int,  # or pl.Int32
            "PARTICIPANTID": pl.Categorical,
            "FILENAME": str,
            "ACKFILENAME": str,
            "AUTHORISEDBY": str,
        }
    )
    # Drop deprecated/unused columns
    .drop("ACKFILENAME", "AUTHORISEDBY")
    # Deduplicate
    # We want one row per ['CONNECTIONPOINTID', 'HOSTDISTRIBUTOR', 'METERINGDATAAGENT', 'METERRUNNO', 'PARTICIPANTID', 'SETTLEMENTDATE']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "CONNECTIONPOINTID",
            "HOSTDISTRIBUTOR",
            "METERINGDATAAGENT",
            "METERRUNNO",
            "PARTICIPANTID",
            "SETTLEMENTDATE",
        ],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Deduplication and PDR Loader Configuration

The primary keys are:

• CONNECTIONPOINTID

• HOSTDISTRIBUTOR

• METERINGDATAAGENT

• METERRUNNO

• PARTICIPANTID

• SETTLEMENTDATE

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
METER_DATA	CUSTOMER_TRK	1

This means that the CSV files start with metadata columns:

I,METER_DATA,CUSTOMER_TRK,1,...
D,METER_DATA,CUSTOMER_TRK,1,...

Indexes

• 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: HISTORICAL TABLES

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