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PERDEMAND

PERDEMAND sets out the regional demands and MR schedule data for each half-hour period. PERDEMAND is a child table to RESDEMANDTRK.

The RESDEMANDTRK and PERDEMAND tables have a parent/child relationship, and define forecast regional demands since market start. RESDEMANDTRK defines the existence and versioning information of a forecast for a specific region and trading date. PERDEMAND defines the numerical forecast values for each trading interval of a the trading day for that region. A complete trading day forecast for one region consists of one RESDEMANDTRK record and 48 PERDEMAND records.

Source

PERDEMAND updates whenever AEMO issues a new or revised forecast. ST PASA forecasts update seven days at a time. Predispatch updates one date.

Volume

1296000 rows per year

Note

In the context of a mandatory restrictions event the forecast schedule (MW) of restrictions are reported through the RESDEMANDTRK and PERDEMAND tables using the new field PerDemand.MR_Schedule. The relationship between fields and mandatory restriction terms for the 50% probability of exceedence forecast are:

· UnRestricted Profile = ResDemand + MR_Schedule

· Restricted Profile = ResDemand

Column Schema

OFFERDATE

Date record issued

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

This column is mandatory, and a primary key.

Example: 2026/08/25 00:00:00

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

PERIODID

Half hourly trading intervals from 04:30.

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

This column is mandatory, and a primary key.

Examples: 1, 48

REGIONID

Differentiates this region from all other regions

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(10). This is actually a factor/enum, with values:

  • QLD1

  • VIC1

  • TAS1

  • NSW1

  • SA1

  • SNOWY1

This column is mandatory, and a primary key.

SETTLEMENTDATE

Market date of forecast up to 7 days ahead.

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

This column is mandatory, and a primary key.

Example: 2026/09/01 00:00:00

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

VERSIONNO

The version of the RESDEMAND file for this date

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

This column is mandatory, and a primary key.

Examples: 2, 48

DEMAND10PROBABILITY

Demand level for a 10% probability of exceedance

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

Examples: 4553, 9671

DEMAND90PROBABILITY

Demand at 90% probability of exceedance

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

Examples: 4128, 8762

EFFECTIVEDATE

Market date the forecast is made for. First date of the 7 days.

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

This column is not mandatory.

Example: 2026/09/01 00:00:00

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

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: 2026/08/25 12:57:10

MR_SCHEDULE

MR_Schedule = Unrestricted Demand - POE

SQL Type: NUMBER(6,0) Polars Type: pl.Int32

This column is not mandatory.

Example: 0

RESDEMAND

Base Demand forecast for period

SQL Type: NUMBER(10,0) Polars Type: pl.Int64

This column is not mandatory.

Examples: 4340, 9217

New Data Timing

When AEMO issues a new or revised forecast. ST PASA forecasts update seven days at a time. Predispatch updates one date.

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("OFFERDATE", "SETTLEMENTDATE", "EFFECTIVEDATE").str.strptime(
            pl.Date, d_fmt
        ),
    )
    .cast(
        {
            "OFFERDATE": pl.Date(),
            "PERIODID": int,  # or pl.Int16
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SETTLEMENTDATE": pl.Date(),
            "VERSIONNO": int,  # or pl.UInt16
            "DEMAND10PROBABILITY": int,  # or pl.Int64
            "DEMAND90PROBABILITY": int,  # or pl.Int64
            "EFFECTIVEDATE": pl.Date(),
            "MR_SCHEDULE": int,  # or pl.Int32
            "RESDEMAND": int,  # or pl.Int64
        }
    )
    # Deduplicate
    # We want one row per ['OFFERDATE', 'PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=["OFFERDATE", "PERIODID", "REGIONID", "SETTLEMENTDATE", "VERSIONNO"],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_PERDEMAND_FILE01_202609010000.zip

EFFECTIVEDATESETTLEMENTDATEREGIONIDOFFERDATEPERIODIDVERSIONNORESDEMANDDEMAND90PROBABILITYDEMAND10PROBABILITYLASTCHANGEDMR_SCHEDULE
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:001266822649371512026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:002266918658472522026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:003267178683175262026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:004267475711278382026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:005267925753983102026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:006268236783586382026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:007268193779385932026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:008267820743882032026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:009267225687175802026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:0010266527620668482026/08/25 12:57:100
2026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:0011265978568362722026/08/25 12:57:100
...

CSETP.WORLDDVD_PERDEMANDAEMOPUBLIC2026/10/0916:26:13001791527173905MONTHLY_ARCHIVE001791527173905
IDEMANDPERIOD1EFFECTIVEDATESETTLEMENTDATEREGIONIDOFFERDATEPERIODIDVERSIONNORESDEMANDDEMAND90PROBABILITYDEMAND10PROBABILITYLASTCHANGEDMR_SCHEDULE
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:001266822649371512026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:002266918658472522026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:003267178683175262026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:004267475711278382026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:005267925753983102026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:006268236783586382026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:007268193779385932026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:008267820743882032026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:009267225687175802026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:0010266527620668482026/08/25 12:57:100
DDEMANDPERIOD12026/09/01 00:00:002026/09/01 00:00:00NSW12026/08/25 00:00:0011265978568362722026/08/25 12:57:100
...
CEND OF REPORT1003

EFFECTIVEDATE,SETTLEMENTDATE,REGIONID,OFFERDATE,PERIODID,VERSIONNO,RESDEMAND,DEMAND90PROBABILITY,DEMAND10PROBABILITY,LASTCHANGED,MR_SCHEDULE
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",1,26,6822,6493,7151,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",2,26,6918,6584,7252,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",3,26,7178,6831,7526,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",4,26,7475,7112,7838,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",5,26,7925,7539,8310,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",6,26,8236,7835,8638,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",7,26,8193,7793,8593,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",8,26,7820,7438,8203,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",9,26,7225,6871,7580,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",10,26,6527,6206,6848,"2026/08/25 12:57:10",0
"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",11,26,5978,5683,6272,"2026/08/25 12:57:10",0
...

C,SETP.WORLD,DVD_PERDEMAND,AEMO,PUBLIC,2026/10/09,16:26:13,001791527173905,MONTHLY_ARCHIVE,001791527173905
I,DEMAND,PERIOD,1,EFFECTIVEDATE,SETTLEMENTDATE,REGIONID,OFFERDATE,PERIODID,VERSIONNO,RESDEMAND,DEMAND90PROBABILITY,DEMAND10PROBABILITY,LASTCHANGED,MR_SCHEDULE
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",1,26,6822,6493,7151,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",2,26,6918,6584,7252,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",3,26,7178,6831,7526,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",4,26,7475,7112,7838,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",5,26,7925,7539,8310,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",6,26,8236,7835,8638,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",7,26,8193,7793,8593,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",8,26,7820,7438,8203,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",9,26,7225,6871,7580,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",10,26,6527,6206,6848,"2026/08/25 12:57:10",0
D,DEMAND,PERIOD,1,"2026/09/01 00:00:00","2026/09/01 00:00:00",NSW1,"2026/08/25 00:00:00",11,26,5978,5683,6272,"2026/08/25 12:57:10",0
...
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 1 of 2015. 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 205 MB when zipped, 2 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
DEMAND PERIOD 1

This means that the CSV files start with metadata columns:

I,DEMAND,PERIOD,1,...
D,DEMAND,PERIOD,1,...

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: DEMAND_FORECASTS