INTERMITTENT_P5_RUN

Unconstrained Intermittent Generation Forecasts (UIGF) for 5-Minute Pre-dispatch. AEMO plans to remove this table in a Data Model version release after 5.6.

Column Schema

DUID

DUID (or Area for non-scheduled) where this forecast applies

Warning

This column may contain funny characters such as / (slash) or # (hash). This may break hive-style partitioning.

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

Example: W/HOE#2

FORECAST_PRIORITY

Unsuppressed forecasts with higher priority values are used in 5-Minute Predispatch in preference to unsuppressed forecasts with lower priority values

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

This column is mandatory, and a primary key.

OFFERDATETIME

Date and Time when this forecast submission was loaded

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

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

ORIGIN

Origin of this forecast (PARTICIPANTID, AWEFS/ASEFS, or another vendor)

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

RUN_DATETIME

Date and Time of the first interval of 5-minute pre-dispatch where the forecast applies.

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

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

AUTHORISEDBY

Authorising officer of this forecast

SQL Type: VARCHAR2(20) Polars Type: str

This column is not mandatory.

COMMENTS

Comments relating to the forecast

SQL Type: VARCHAR2(200) Polars Type: str

This column is not mandatory.

LASTCHANGED

Last date and time the 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

MODEL

Metadata relating to the forecast.

SQL Type: VARCHAR2(30) Polars Type: str

This column is not mandatory.

PARTICIPANT_TIMESTAMP

Participant can document when the forecast was created

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

This column is not mandatory.

Example: 2026/01/20 00:05:00

SUPPRESSED_AEMO

Was this forecast suppressed by AEMO? Suppressed = 1,Not suppressed =0

SQL Type: NUMBER(1,0) Polars Type: pl.Int8

This column is not mandatory.

SUPPRESSED_PARTICIPANT

Was this forecast suppressed by the participant? Suppressed submissions may not be used, Suppressed = 1, Not suppressed =0

SQL Type: NUMBER(1,0) Polars Type: pl.Int8

This column is not mandatory.

TRANSACTION_ID

Uniquely identifies this interaction

SQL Type: VARCHAR2(100) Polars Type: str

This column is not mandatory.

New Data Timing

Publication of 5-Minute Predispatch solution. Data includes all valid forecasts for that 5-Minute Predispatch ru

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(
            "OFFERDATETIME", "RUN_DATETIME", "LASTCHANGED", "PARTICIPANT_TIMESTAMP"
        ).str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "DUID": pl.Categorical,
            "FORECAST_PRIORITY": int,  # or pl.Int64
            "ORIGIN": str,
            "AUTHORISEDBY": str,
            "COMMENTS": str,
            "MODEL": str,
            "SUPPRESSED_AEMO": int,  # or pl.Int8
            "SUPPRESSED_PARTICIPANT": int,  # or pl.Int8
            "TRANSACTION_ID": str,
        }
    )
    # Deduplicate
    # We want one row per ['DUID', 'FORECAST_PRIORITY', 'OFFERDATETIME', 'ORIGIN', 'RUN_DATETIME']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["DUID", "FORECAST_PRIORITY", "OFFERDATETIME", "ORIGIN", "RUN_DATETIME"],
        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:

• DUID

• FORECAST_PRIORITY

• OFFERDATETIME

• ORIGIN

• RUN_DATETIME

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.

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: DEMAND_FORECASTS

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