GENMETER

GENMETER shows details of generator meter sets.

GENMETER is a public table, and is available to all participants.

Source

GENMETER updates only when meter details change.

Column Schema

APPLYDATE

Application 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

METERID

Meter Id

SQL Type: VARCHAR2(12) Polars Type: str

This column is mandatory, and a primary key.

VERSIONNO

Version no of the record for the given effective date

SQL Type: NUMBER(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

AUTHORISEDBY

AEMO user authorising

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

AUTHORISEDDATE

Date 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: 2026/01/20 00:05:00

GENSETID

Generator Set ID

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

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

METERCLASS

WATT or AUXILARY

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

METERTYPE

LOAD

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is not mandatory.

STATIONID

Station Identifier

For more information, see the dedicated documentation page.

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

This column is not mandatory.

VOLTAGELEVEL

Voltage

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

This column is not mandatory.

COMDATE

Not used

Warning

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

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

This column is unused.

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

CONNECTIONPOINTID

Not used

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

This column is unused.

DECOMDATE

Not used

Warning

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

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

This column is unused.

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

ENDDATE

Not used

Warning

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

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

This column is unused.

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

STARTDATE

Not used

Warning

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

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

This column is unused.

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

New Data Timing

When meter details change

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",
    )
    .with_columns(
        pl.col(
            "APPLYDATE",
            "AUTHORISEDDATE",
            "LASTCHANGED",
            "COMDATE",
            "DECOMDATE",
            "ENDDATE",
            "STARTDATE",
        ).str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "METERID": str,
            "VERSIONNO": int,  # or pl.UInt16
            "AUTHORISEDBY": str,
            "GENSETID": pl.Categorical,
            "METERCLASS": str,
            "METERTYPE": pl.Categorical,
            "STATIONID": pl.Categorical,
            "VOLTAGELEVEL": int,  # or pl.Int32
            "CONNECTIONPOINTID": pl.Categorical,
        }
    )
    # Drop deprecated/unused columns
    .drop("COMDATE", "CONNECTIONPOINTID", "DECOMDATE", "ENDDATE", "STARTDATE")
    # Deduplicate
    # We want one row per ['APPLYDATE', 'METERID', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["APPLYDATE", "METERID", "VERSIONNO"], 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:

APPLYDATE
METERID
VERSIONNO

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
PARTICIPANT_REGISTRATION	GENMETER	1

This means that the CSV files start with metadata columns:

I,PARTICIPANT_REGISTRATION,GENMETER,1,...
D,PARTICIPANT_REGISTRATION,GENMETER,1,...

Indexes

There is more than one index.

Index 1:

LASTCHANGED

Index 2:

STATIONID

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: PARTICIPANT_REGISTRATION