Skip to content

BILLING_GST_DETAIL

BILLING_GST_DETAIL shows the BAS class, GST_Exclusive and GST amount (if any) attributable to a participant for each transaction type.

BILLING_GST_DETAIL data is confidential to NSP participants.

Source

Populated by the posting of a billing run.

Volume

Approximately 20 records are inserted per billrunno, or about 220 records inserted per week.

Column Schema

BAS_CLASS

The BAS classification that the transaction type belongs to.

SQL Type: VARCHAR2(30) Polars Type: str

This column is mandatory, and a primary key.

BILLRUNNO

Unique run no within a given contract year and week no

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

This column is mandatory, and a primary key.

CONTRACTYEAR

AEMO Contract Year number starting in week containing 1st January

SQL Type: NUMBER(4,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

PARTICIPANTID

Unique participant identifier

For more information, see the dedicated documentation page.

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

This column is mandatory, and a primary key.

TRANSACTION_TYPE

The transaction type (e.g. CUSTOMER_ENERGY_PURCHASES)

SQL Type: VARCHAR2(30) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

WEEKNO

Week no within the contract year. Week no 1 is the week containing 1st January

Warning

There may be 53 weeks in a year, depending on how weeks align to the year start/end.

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

This column is mandatory, and a primary key.

GST_AMOUNT

The GST amount for this transaction type.

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

This column is not mandatory.

GST_EXCLUSIVE_AMOUNT

The GST exclusive amount paid by/to the participant to/by AEMO for this transaction type.

SQL Type: NUMBER(15,5) Polars Type: pl.Float64

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

New Data Timing

Generated when a billing run is posted

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("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "BAS_CLASS": str,
            "BILLRUNNO": int,  # or pl.UInt16
            "CONTRACTYEAR": int,  # or pl.UInt16
            "PARTICIPANTID": pl.Categorical,
            "TRANSACTION_TYPE": pl.Categorical,
            "WEEKNO": int,  # or pl.UInt16
            "GST_AMOUNT": pl.Float64,
            "GST_EXCLUSIVE_AMOUNT": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['BAS_CLASS', 'BILLRUNNO', 'CONTRACTYEAR', 'PARTICIPANTID', 'TRANSACTION_TYPE', 'WEEKNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "BAS_CLASS",
            "BILLRUNNO",
            "CONTRACTYEAR",
            "PARTICIPANTID",
            "TRANSACTION_TYPE",
            "WEEKNO",
        ],
        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
BILLING GST_DETAIL 5

This means that the CSV files start with metadata columns:

I,BILLING,GST_DETAIL,5,...
D,BILLING,GST_DETAIL,5,...

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: BILLING_RUN