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DEMANDOPERATIONALFORECAST

Shows Forecast Operational Demand for a particular date time interval.

Column Schema

INTERVAL_DATETIME

Forecast for a particular date time interval

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

This column is mandatory, and a primary key.

Example: 2026/09/01 00:30:00

REGIONID

Region identifier

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

  • QLD1

  • VIC1

  • TAS1

  • NSW1

  • SA1

  • SNOWY1

This column is mandatory, and a primary key.

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: 2026/08/31 23:57:09

LOAD_DATE

Date time this forecast was produced

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is not mandatory.

Example: 2026/08/31 23:57:09

OPERATIONAL_DEMAND_POE10

10% probability of exceedance operational demand forecast value

SQL Type: NUMBER(15,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 4885.11, 762, 5423.1, 9120

OPERATIONAL_DEMAND_POE50

50% probability of exceedance operational demand forecast value

SQL Type: NUMBER(15,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 730, 8994

OPERATIONAL_DEMAND_POE90

90% probability of exceedance operational demand forecast value

SQL Type: NUMBER(15,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 4744.89, 698, 5265.9, 8869

New Data Timing

Ad hoc

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("INTERVAL_DATETIME", "LASTCHANGED", "LOAD_DATE").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "OPERATIONAL_DEMAND_POE10": pl.Float64,
            "OPERATIONAL_DEMAND_POE50": pl.Float64,
            "OPERATIONAL_DEMAND_POE90": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['INTERVAL_DATETIME', 'REGIONID']
    # Tie-breaker: greatest value of ['INTERVAL_DATETIME', 'REGIONID']
    .sort(["INTERVAL_DATETIME", "REGIONID"], descending=False)
    .unique(subset=["INTERVAL_DATETIME", "REGIONID"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_DEMANDOPERATIONALFORECAST_FILE01_202609010000.zip

INTERVAL_DATETIMEREGIONIDLOAD_DATEOPERATIONAL_DEMAND_POE10OPERATIONAL_DEMAND_POE50OPERATIONAL_DEMAND_POE90LASTCHANGED
2026/09/01 00:30:00NSW12026/08/31 23:57:097790768275752026/08/31 23:57:09
2026/09/01 00:30:00QLD12026/08/31 23:57:095863577856942026/08/31 23:57:09
2026/09/01 00:30:00SA12026/08/31 23:57:091486142413622026/08/31 23:57:09
2026/09/01 00:30:00TAS12026/08/31 23:57:0910099919712026/08/31 23:57:09
2026/09/01 00:30:00VIC12026/08/31 23:57:095656553854192026/08/31 23:57:09
2026/09/01 01:00:00NSW12026/09/01 00:27:097810770275952026/09/01 00:27:09
2026/09/01 01:00:00QLD12026/09/01 00:27:095742566055762026/09/01 00:27:09
2026/09/01 01:00:00SA12026/09/01 00:27:091520145713942026/09/01 00:27:09
2026/09/01 01:00:00TAS12026/09/01 00:27:099999819632026/09/01 00:27:09
2026/09/01 01:00:00VIC12026/09/01 00:27:095562544553292026/09/01 00:27:09
2026/09/01 01:30:00NSW12026/09/01 00:57:097615751074052026/09/01 00:57:09
...

CSETP.WORLDDVD_DEMANDOPERATIONALFORECASTAEMOPUBLIC2026/10/0915:25:04001791523504180MONTHLY_ARCHIVE001791523504180
IOPERATIONAL_DEMANDFORECAST1INTERVAL_DATETIMEREGIONIDLOAD_DATEOPERATIONAL_DEMAND_POE10OPERATIONAL_DEMAND_POE50OPERATIONAL_DEMAND_POE90LASTCHANGED
DOPERATIONAL_DEMANDFORECAST12026/09/01 00:30:00NSW12026/08/31 23:57:097790768275752026/08/31 23:57:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 00:30:00QLD12026/08/31 23:57:095863577856942026/08/31 23:57:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 00:30:00SA12026/08/31 23:57:091486142413622026/08/31 23:57:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 00:30:00TAS12026/08/31 23:57:0910099919712026/08/31 23:57:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 00:30:00VIC12026/08/31 23:57:095656553854192026/08/31 23:57:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 01:00:00NSW12026/09/01 00:27:097810770275952026/09/01 00:27:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 01:00:00QLD12026/09/01 00:27:095742566055762026/09/01 00:27:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 01:00:00SA12026/09/01 00:27:091520145713942026/09/01 00:27:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 01:00:00TAS12026/09/01 00:27:099999819632026/09/01 00:27:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 01:00:00VIC12026/09/01 00:27:095562544553292026/09/01 00:27:09
DOPERATIONAL_DEMANDFORECAST12026/09/01 01:30:00NSW12026/09/01 00:57:097615751074052026/09/01 00:57:09
...
CEND OF REPORT1003

INTERVAL_DATETIME,REGIONID,LOAD_DATE,OPERATIONAL_DEMAND_POE10,OPERATIONAL_DEMAND_POE50,OPERATIONAL_DEMAND_POE90,LASTCHANGED
"2026/09/01 00:30:00",NSW1,"2026/08/31 23:57:09",7790,7682,7575,"2026/08/31 23:57:09"
"2026/09/01 00:30:00",QLD1,"2026/08/31 23:57:09",5863,5778,5694,"2026/08/31 23:57:09"
"2026/09/01 00:30:00",SA1,"2026/08/31 23:57:09",1486,1424,1362,"2026/08/31 23:57:09"
"2026/09/01 00:30:00",TAS1,"2026/08/31 23:57:09",1009,991,971,"2026/08/31 23:57:09"
"2026/09/01 00:30:00",VIC1,"2026/08/31 23:57:09",5656,5538,5419,"2026/08/31 23:57:09"
"2026/09/01 01:00:00",NSW1,"2026/09/01 00:27:09",7810,7702,7595,"2026/09/01 00:27:09"
"2026/09/01 01:00:00",QLD1,"2026/09/01 00:27:09",5742,5660,5576,"2026/09/01 00:27:09"
"2026/09/01 01:00:00",SA1,"2026/09/01 00:27:09",1520,1457,1394,"2026/09/01 00:27:09"
"2026/09/01 01:00:00",TAS1,"2026/09/01 00:27:09",999,981,963,"2026/09/01 00:27:09"
"2026/09/01 01:00:00",VIC1,"2026/09/01 00:27:09",5562,5445,5329,"2026/09/01 00:27:09"
"2026/09/01 01:30:00",NSW1,"2026/09/01 00:57:09",7615,7510,7405,"2026/09/01 00:57:09"
...

C,SETP.WORLD,DVD_DEMANDOPERATIONALFORECAST,AEMO,PUBLIC,2026/10/09,15:25:04,001791523504180,MONTHLY_ARCHIVE,001791523504180
I,OPERATIONAL_DEMAND,FORECAST,1,INTERVAL_DATETIME,REGIONID,LOAD_DATE,OPERATIONAL_DEMAND_POE10,OPERATIONAL_DEMAND_POE50,OPERATIONAL_DEMAND_POE90,LASTCHANGED
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 00:30:00",NSW1,"2026/08/31 23:57:09",7790,7682,7575,"2026/08/31 23:57:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 00:30:00",QLD1,"2026/08/31 23:57:09",5863,5778,5694,"2026/08/31 23:57:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 00:30:00",SA1,"2026/08/31 23:57:09",1486,1424,1362,"2026/08/31 23:57:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 00:30:00",TAS1,"2026/08/31 23:57:09",1009,991,971,"2026/08/31 23:57:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 00:30:00",VIC1,"2026/08/31 23:57:09",5656,5538,5419,"2026/08/31 23:57:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 01:00:00",NSW1,"2026/09/01 00:27:09",7810,7702,7595,"2026/09/01 00:27:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 01:00:00",QLD1,"2026/09/01 00:27:09",5742,5660,5576,"2026/09/01 00:27:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 01:00:00",SA1,"2026/09/01 00:27:09",1520,1457,1394,"2026/09/01 00:27:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 01:00:00",TAS1,"2026/09/01 00:27:09",999,981,963,"2026/09/01 00:27:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 01:00:00",VIC1,"2026/09/01 00:27:09",5562,5445,5329,"2026/09/01 00:27:09"
D,OPERATIONAL_DEMAND,FORECAST,1,"2026/09/01 01:30:00",NSW1,"2026/09/01 00:57:09",7615,7510,7405,"2026/09/01 00:57:09"
...
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 1 MB when zipped, 13 MB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "UPDATE-INSERT", which means that you will likely see duplicate data with different values.

You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.

PDR Loader configuration is:

Report Name Report Subtype Version
OPERATIONAL_DEMAND FORECAST 1

This means that the CSV files start with metadata columns:

I,OPERATIONAL_DEMAND,FORECAST,1,...
D,OPERATIONAL_DEMAND,FORECAST,1,...

ROW_FILTER_TYPE is LASTCHANGED

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