SETIRFMRECOVERY
SETIRFMRECOVERY sets out reimbursements for Industrial Relations Force Majeure to be recovered from participants.
Deprecation Warning
This table is in the HISTORICAL TABLES package, so is likely deprecated.
SETIRFMRECOVERY data is confidential to the relevant participant.
Source
SETIRFMRECOVERY updates with each settlement run.
Column Schema
IRFMID
Industrial Relations Forced Majeure event number
SQL Type: VARCHAR2(10)
Polars Type: str
This column is mandatory, and a primary key.
PARTICIPANTID
Participant unique 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.
PERIODID
Settlement period ID
SQL Type: NUMBER(3,0)
Polars Type: pl.Int16
This column is mandatory, and a primary key.
SETTLEMENTDATE
Settlement 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
VERSIONNO
Version number
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
IRFMPAYMENT
Industrial Relations Forced Majeure payment amount in $.
SQL Type: NUMBER(12,5)
Polars Type: pl.Float64
This column is not mandatory.
IRMFVERSION
Industrial Relations Forced Majeure event number
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
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
PARTICIPANTDEMAND
Participant demand
SQL Type: NUMBER(12,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALTCD
Total non franchised load in Victoria.
SQL Type: NUMBER(12,5)
Polars Type: pl.Float64
This column is not mandatory.
TOTALTFD
Total franchised load in Victoria.
SQL Type: NUMBER(12,5)
Polars Type: pl.Float64
This column is not mandatory.
IRFMAMOUNT
Industrial Relations Forced Majeure event amount in $.
SQL Type: NUMBER(12,5)
Polars Type: pl.Float64
This column is unused.
New Data Timing
Each settlement 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", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"IRFMID": str,
"PARTICIPANTID": pl.Categorical,
"PERIODID": int, # or pl.Int16
"VERSIONNO": int, # or pl.UInt16
"IRFMPAYMENT": pl.Float64,
"IRMFVERSION": int, # or pl.UInt16
"PARTICIPANTDEMAND": pl.Float64,
"TOTALTCD": pl.Float64,
"TOTALTFD": pl.Float64,
"IRFMAMOUNT": pl.Float64,
}
)
# Drop deprecated/unused columns
.drop("IRFMAMOUNT")
# Deduplicate
# We want one row per ['IRFMID', 'PARTICIPANTID', 'PERIODID', 'SETTLEMENTDATE', 'VERSIONNO']
# Tie-breaker: greatest value of ['LASTCHANGED']
.sort(["LASTCHANGED"], descending=False)
.unique(
subset=["IRFMID", "PARTICIPANTID", "PERIODID", "SETTLEMENTDATE", "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:
PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.
You should probably take the row with the maximum LASTCHANGED.
Indexes
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