AREA
Table containing static metadata for the Areas, which are sub-regions used in load forecasting and rooftop PV forecasting. The latest metadata can be obtained for each AreaID using the most recent EffectiveDate and then VersionNo. If an AreaID is not mapped to an active RegionID in the corresponding
REGION_AREAtable, then that AreaID can be considered inactive.
Column Schema
AREAID
Area identifier
SQL Type: VARCHAR2(10)
Polars Type: str
This column is mandatory, and a primary key.
EFFECTIVEDATE
Calendar date from when this record set is effective.
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 for the same effectivedate.
SQL Type: NUMBER(3,0)
Polars Type: pl.UInt16
This column is mandatory, and a primary key.
AREA_DESCRIPTION
Area description
SQL Type: VARCHAR2(200)
Polars Type: str
This column is not mandatory.
AREA_NAME
Area name
SQL Type: VARCHAR2(20)
Polars Type: str
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
Ad hoc when Areas are updated or added
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("EFFECTIVEDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"AREAID": str,
"VERSIONNO": int, # or pl.UInt16
"AREA_DESCRIPTION": str,
"AREA_NAME": str,
}
)
# Deduplicate
# We want one row per ['AREAID', 'EFFECTIVEDATE', 'VERSIONNO']
# Tie-breaker: greatest value of LASTCHANGED
.sort(LASTCHANGED, descending=False)
.unique(subset=["AREAID", "EFFECTIVEDATE", "VERSIONNO"], keep="last")
)
Nemosis
This table is not yet available in Nemosis.
Availability
AEMO's documentation states that this table's visibility is "Public". However, it is not available in the public monthly MMSDM archive. AEMO probably call this public because it is available to all market participants over private FTP.
There is a chance that is is still public to non-participants. It may only be available for some months; or (rarely) it may be available in a non-standard location.
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 |
|---|---|---|
| MARKET_CONFIG | AREA | 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: MARKET_CONFIG