ROOFTOP_PV_ACTUAL_PRED

Contains predictions for rooftop PV area estimated actuals, with a 5-minute and 30-minute resolution for different estimate types. This is the child table of the parent table ROOFTOP_PV_ACTUAL_RUN, which contains the corresponding actual prediction runs.

Column Schema

AREAID

Area identifier, aligning with the load forecasting areas.

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

ESTIMATE_TYPE

Type of Rooftop PV estimate, for example, MEASURED, SATELLITE and so on.

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

This column is mandatory, and a primary key.

INTERVAL_DATETIME

Date and Time the forecast applies (dispatch interval ending).

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

This column is mandatory, and a primary key.

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

INTERVAL_DURATION

Duration of each interval (in minutes) for this prediction, for example, 5 or 30.

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

This column is mandatory, and a primary key.

OFFERDATETIME

Datetime when this prediction 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

PREDICTION_PRIORITY

Priority of prediction run, higher number is used in preference to lower number for the same provider.

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

This column is mandatory, and a primary key.

PREDICTION_RUN_DATETIME

Datetime (interval ending) from which this prediction run is valid.

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

This column is mandatory, and a primary key.

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

PROVIDERID

Provider identifier, for example, AEMO, PROVIDER_A and so on.

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

This column is mandatory, and a primary key.

PREDICTION_QUALITY

Prediction quality. Higher number represents better quality.

SQL Type: NUMBER(2,0) Polars Type: pl.UInt8

This column is not mandatory.

PREDICTION_VALUE

Prediction value in MW.

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

This column is not mandatory.

New Data Timing

Every 5 or 30 minutes when a new rooftop PV estimated actual is available, covering the most recent 5 or 30-minute interval that is available

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", "OFFERDATETIME", "PREDICTION_RUN_DATETIME"
        ).str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "AREAID": pl.Categorical,
            "ESTIMATE_TYPE": pl.Categorical,
            "INTERVAL_DURATION": int,  # or pl.UInt16
            "PREDICTION_PRIORITY": int,  # or pl.Int64
            "PROVIDERID": pl.Categorical,
            "PREDICTION_QUALITY": int,  # or pl.UInt8
            "PREDICTION_VALUE": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['AREAID', 'ESTIMATE_TYPE', 'INTERVAL_DATETIME', 'INTERVAL_DURATION', 'OFFERDATETIME', 'PREDICTION_PRIORITY', 'PREDICTION_RUN_DATETIME', 'PROVIDERID']
    .unique(
        subset=[
            "AREAID",
            "ESTIMATE_TYPE",
            "INTERVAL_DATETIME",
            "INTERVAL_DURATION",
            "OFFERDATETIME",
            "PREDICTION_PRIORITY",
            "PREDICTION_RUN_DATETIME",
            "PROVIDERID",
        ],
        keep="any",
    )
)

Nemosis

This table is not yet available in Nemosis.

Availability

AEMO's documentation states that this table's visibility is "Public". However, it is not available in the public monthly MMSDM archive. AEMO probably call this public because it is available to all market participants over private FTP.

There is a chance that is is still public to non-participants. It may only be available for some months; or (rarely) it may be available in a non-standard location.

Deduplication and PDR Loader Configuration

The primary keys are:

• AREAID

• ESTIMATE_TYPE

• INTERVAL_DATETIME

• INTERVAL_DURATION

• OFFERDATETIME

• PREDICTION_PRIORITY

• PREDICTION_RUN_DATETIME

• PROVIDERID

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	ROOFTOP_PV_ACTUAL_PRED	1

This means that the CSV files start with metadata columns:

I,DEMAND,ROOFTOP_PV_ACTUAL_PRED,1,...
D,DEMAND,ROOFTOP_PV_ACTUAL_PRED,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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