Skip to content

METERDATA_INDIVIDUAL_READS

Publishes metering data associated with individual metering points for a given CASE_ID

Column Schema

CASE_ID

Case Identifier

SQL Type: NUMBER(15,0) Polars Type: pl.Int64

This column is mandatory, and a primary key.

METER_ID

The National Metering Identifier (NMI)

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

METER_ID_SUFFIX

The National Metering Identifier (NMI) data stream

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

PERIODID

Trading Interval.

SQL Type: NUMBER(3,0) Polars Type: pl.Int16

This column is mandatory, and a primary key.

SETTLEMENTDATE

Settlement date within the case

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

CONNECTIONPOINTID

Connection Point ID

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

This column is mandatory (but not a primary key).

EXPORTVALUE

The export(pool-centric) value for the meter read (MWh)

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is mandatory (but not a primary key).

FRMP

The financially responsible market participantid

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory (but not a primary key).

IMPORTVALUE

The import(pool-centric) value for the meter read (MWh)

SQL Type: NUMBER(18,8) Polars Type: pl.Float64

This column is mandatory (but not a primary key).

LASTCHANGED

Last changed date for the record

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

LR

The local retailer at the connection point id

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory (but not a primary key).

METER_TYPE

The meter type for the read, one of: CUSTOMER; GENERATOR; EMBEDDED_GENERATOR

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

This column is mandatory (but not a primary key).

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("SETTLEMENTDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "CASE_ID": int,  # or pl.Int64
            "METER_ID": str,
            "METER_ID_SUFFIX": str,
            "PERIODID": int,  # or pl.Int16
            "CONNECTIONPOINTID": pl.Categorical,
            "EXPORTVALUE": pl.Float64,
            "FRMP": str,
            "IMPORTVALUE": pl.Float64,
            "LR": str,
            "METER_TYPE": pl.Categorical,
        }
    )
    # Deduplicate
    # We want one row per ['CASE_ID', 'METER_ID', 'METER_ID_SUFFIX', 'PERIODID', 'SETTLEMENTDATE']
    # Tie-breaker: greatest value of ['CASE_ID', 'SETTLEMENTDATE', 'METER_ID', 'METER_ID_SUFFIX', 'PERIODID']
    .sort(
        ["CASE_ID", "SETTLEMENTDATE", "METER_ID", "METER_ID_SUFFIX", "PERIODID"],
        descending=False,
    )
    .unique(
        subset=["CASE_ID", "METER_ID", "METER_ID_SUFFIX", "PERIODID", "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:

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
METERDATA INDIVIDUAL_READS 1

This means that the CSV files start with metadata columns:

I,METERDATA,INDIVIDUAL_READS,1,...
D,METERDATA,INDIVIDUAL_READS,1,...

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: METER_DATA