METERDATA_WDR_READS
Metering Data WDR Readings
Column Schema
CASE_ID
Unique identifier for the metering case.
SQL Type: NUMBER(15,0)
Polars Type: pl.Int64
This column is mandatory, and a primary key.
MARKET_ID
Unique identifier for the market to which this metering record applies. Always equal to NEM in the current system.
SQL Type: VARCHAR2(20)
Polars Type: str
This column is mandatory, and a primary key.
METER_ID
Unique identifier for the meter to which the metering record applies
SQL Type: VARCHAR2(20)
Polars Type: str
This column is mandatory, and a primary key.
PERIODID
Trading interval identifier, with Period 1 being the first TI for the calendar day, i.e interval ending 00:05.
SQL Type: NUMBER(3,0)
Polars Type: pl.Int16
This column is mandatory, and a primary key.
SETTLEMENTDATE
The settlement date for the metering record
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
BASELINECALCULATIONID
A reference to the baseline run that produced the baseline quantity for this NMI and interval
SQL Type: VARCHAR2(100)
Polars Type: str
This column is not mandatory.
BASELINEQUANTITY
Baseline quantity in MWh for the NMI in the trading interval. A negative value indicates net consumption, while a positive value indicates the net generation
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
DRSP
Unique identifier for the participant acting as the DRSP for this NMI on the settlement date
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
FRMP
Unique identifier for the participant acting as the FRMP for this NMI on the settlement date
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
ISNONCOMPLIANT
A value of TRUE (indicated by 1) for this column indicates that financial settlement of WDR transactions for this NMI should not proceed for the settlement date and trading interval. Possible values are 1 and 0.
SQL Type: NUMBER(1,0)
Polars Type: pl.Int8
This column is not mandatory.
METEREDQUANTITYEXPORT
Metered quantity Export in MWh for the NMI in the trading interval. A negative value indicates net consumption, while a positive value indicates net generation
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
METEREDQUANTITYIMPORT
Metered quantity Import in MWh for the NMI in the trading interval. A negative value indicates net consumption, while a positive value indicates net generation
SQL Type: NUMBER(18,8)
Polars Type: pl.Float64
This column is not mandatory.
QUALITYFLAG
Quality flag for the meter read. Where multiple datastreams exist against the NMI with different quality flags for each read, the lowest quality flag will be published against the NMI for the interval.
SQL Type: VARCHAR2(20)
Polars Type: pl.Boolean
This column is not mandatory.
TNI
Unique identifier for the transmission node to which this meter belongs on the settlement date
SQL Type: VARCHAR2(20)
Polars Type: str
This column is not mandatory.
New Data Timing
Posting of a billing 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"
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").str.strptime(pl.Datetime, dt_s_fmt),
)
# Polars can't cast string straight to bool. Need to go via something else.
.cast({"QUALITYFLAG": pl.UInt8})
.cast(
{
"CASE_ID": int, # or pl.Int64
"MARKET_ID": str,
"METER_ID": str,
"PERIODID": int, # or pl.Int16
"BASELINECALCULATIONID": str,
"BASELINEQUANTITY": pl.Float64,
"DRSP": str,
"FRMP": str,
"ISNONCOMPLIANT": int, # or pl.Int8
"METEREDQUANTITYEXPORT": pl.Float64,
"METEREDQUANTITYIMPORT": pl.Float64,
"QUALITYFLAG": pl.Boolean,
"TNI": str,
}
)
# Deduplicate
# We want one row per ['CASE_ID', 'MARKET_ID', 'METER_ID', 'PERIODID', 'SETTLEMENTDATE']
.unique(
subset=["CASE_ID", "MARKET_ID", "METER_ID", "PERIODID", "SETTLEMENTDATE"],
keep="any",
)
)
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.
| Report Name | Report Subtype | Version |
|---|---|---|
| METERDATA | WDR_READS | 1 |
This means that the CSV files start with metadata columns:
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: METER_DATA