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SETLOCALAREAENERGY

SETLOCALAREAENERGY shows the UFE, AGE and associated values for each local area and trading interval in a settlement run.

Column Schema

LOCALAREAID

Unique identifier for the local area

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

This column is mandatory, and a primary key.

Examples:

  • NSW1
  • QLD1
  • SA1
  • TAS1

PERIODID

Settlement Trading Interval

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

This column is mandatory, and a primary key.

Examples: 1, 288

SETTLEMENTDATE

Settlement date of the settlement run

SQL Type: DATE Polars Type: pl.Date()

This column is mandatory, and a primary key.

Example: 2026/08/31 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

SETTLEMENTRUNNO

Settlement run number of the settlement run

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

This column is mandatory, and a primary key.

Example: 1

ADME

ADME component of UFE for the local area in this trading interval, in MWh.

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

This column is not mandatory.

Example: 0

ADMELA

The sum of all DME amounts for each Market Customer FRMP and TNI in the local area, in this trading interval.

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

This column is not mandatory.

Example: 0

DDME

DDME component of UFE for the local area in this trading interval, in MWh.

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

This column is not mandatory.

Example: 0

LASTCHANGED

Last changed date time for the record

For more information, see the dedicated documentation page.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2026/09/01 01:12:12

TME

TME component of UFE for the local area in this trading interval, in MWh.

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

This column is not mandatory.

Example: 0

UFE

Total unaccounted-for energy for the local area in this trading interval, in MWh

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

This column is not mandatory.

Example: 0

New Data Timing

After 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"

# Dates are represented as datetimes at midnight

d_fmt = "%Y/%m/%d 00:00:00"

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),
        pl.col("SETTLEMENTDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "LOCALAREAID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "SETTLEMENTDATE": pl.Date(),
            "SETTLEMENTRUNNO": int,  # or pl.UInt16
            "ADME": pl.Float64,
            "ADMELA": pl.Float64,
            "DDME": pl.Float64,
            "TME": pl.Float64,
            "UFE": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['LOCALAREAID', 'PERIODID', 'SETTLEMENTDATE', 'SETTLEMENTRUNNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["LOCALAREAID", "PERIODID", "SETTLEMENTDATE", "SETTLEMENTRUNNO"],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_SETLOCALAREAENERGY_FILE01_202609010000.zip

SETTLEMENTDATESETTLEMENTRUNNOLOCALAREAIDPERIODIDUFEDDMETMEADMEADMELALASTCHANGED
2026/08/31 00:00:001NSW11000002026/09/01 01:12:12
2026/08/31 00:00:001NSW12000002026/09/01 01:12:12
2026/08/31 00:00:001NSW13000002026/09/01 01:12:12
2026/08/31 00:00:001NSW14000002026/09/01 01:12:12
2026/08/31 00:00:001NSW15000002026/09/01 01:12:12
2026/08/31 00:00:001NSW16000002026/09/01 01:12:12
2026/08/31 00:00:001NSW17000002026/09/01 01:12:12
2026/08/31 00:00:001NSW18000002026/09/01 01:12:12
2026/08/31 00:00:001NSW19000002026/09/01 01:12:12
2026/08/31 00:00:001NSW110000002026/09/01 01:12:12
2026/08/31 00:00:001NSW111000002026/09/01 01:12:12
...

CSETP.WORLDDVD_SETLOCALAREAENERGYAEMOPUBLIC2026/10/0916:33:21001791527601306MONTHLY_ARCHIVE001791527601306
ISETTLEMENTSLOCALAREAENERGY1SETTLEMENTDATESETTLEMENTRUNNOLOCALAREAIDPERIODIDUFEDDMETMEADMEADMELALASTCHANGED
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW11000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW12000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW13000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW14000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW15000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW16000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW17000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW18000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW19000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW110000002026/09/01 01:12:12
DSETTLEMENTSLOCALAREAENERGY12026/08/31 00:00:001NSW111000002026/09/01 01:12:12
...
CEND OF REPORT1003

SETTLEMENTDATE,SETTLEMENTRUNNO,LOCALAREAID,PERIODID,UFE,DDME,TME,ADME,ADMELA,LASTCHANGED
"2026/08/31 00:00:00",1,NSW1,1,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,2,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,3,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,4,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,5,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,6,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,7,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,8,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,9,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,10,0,0,0,0,0,"2026/09/01 01:12:12"
"2026/08/31 00:00:00",1,NSW1,11,0,0,0,0,0,"2026/09/01 01:12:12"
...

C,SETP.WORLD,DVD_SETLOCALAREAENERGY,AEMO,PUBLIC,2026/10/09,16:33:21,001791527601306,MONTHLY_ARCHIVE,001791527601306
I,SETTLEMENTS,LOCALAREAENERGY,1,SETTLEMENTDATE,SETTLEMENTRUNNO,LOCALAREAID,PERIODID,UFE,DDME,TME,ADME,ADMELA,LASTCHANGED
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,1,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,2,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,3,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,4,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,5,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,6,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,7,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,8,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,9,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,10,0,0,0,0,0,"2026/09/01 01:12:12"
D,SETTLEMENTS,LOCALAREAENERGY,1,"2026/08/31 00:00:00",1,NSW1,11,0,0,0,0,0,"2026/09/01 01:12:12"
...
C,"END OF REPORT",1003

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 8 of 2024. The most recent available data is month 9 of 2026.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 255 MB when zipped, 3 GB as unzipped CSV.

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
SETTLEMENTS LOCALAREAENERGY 1

This means that the CSV files start with metadata columns:

I,SETTLEMENTS,LOCALAREAENERGY,1,...
D,SETTLEMENTS,LOCALAREAENERGY,1,...

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: SETTLEMENT_DATA