INTERMITTENT_GEN_FCST

Identifying record for a given forecast of an intermittent generation. This table is the version table for the INTERMITTENT_GEN_FCST_DATA table which stores the individual forecast values. AEMO plans to remove this table in a Data Model version release after 5.6.

Source

INTERMITTENT_GEN_FCST_DATA updates every 30 minutes when AEMO issues a new 30-minute forecast of intermittent generation out to 8 days ahead.

Volume

~18,000 rows per generator per year

Column Schema

DUID

Identifier of the intermittent generator.

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

RUN_DATETIME

Date Time of forecast (AEST).

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

This column is mandatory, and a primary key.

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

END_INTERVAL_DATETIME

Date Time (AEST) of the final half-hour interval being forecast.

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

This column is mandatory (but not a primary key).

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

LASTCHANGED

Date Time record was created

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

START_INTERVAL_DATETIME

Date Time (AEST) of the first half-hour interval being forecast.

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

This column is mandatory (but not a primary key).

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

VERSIONNO

Versioning information for resolution back to AEMO's wind generation forecasting system.

SQL Type: NUMBER(10,0) Polars Type: pl.UInt64

This column is not mandatory.

New Data Timing

Every 30 minutes when AEMO issues a new 30-minute forecast of intermittent generation out to 8 days ahead

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(
            "RUN_DATETIME",
            "END_INTERVAL_DATETIME",
            "LASTCHANGED",
            "START_INTERVAL_DATETIME",
        ).str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast({"DUID": pl.Categorical, "VERSIONNO": int})  # or pl.UInt64
    # Deduplicate
    # We want one row per ['DUID', 'RUN_DATETIME']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(subset=["DUID", "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

• RUN_DATETIME

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
FORECAST	INTERMITTENT_GEN	1

This means that the CSV files start with metadata columns:

I,FORECAST,INTERMITTENT_GEN,1,...
D,FORECAST,INTERMITTENT_GEN,1,...

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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