P5MIN_ROOFTOP_PV_FCST_TRK
Uniquely tracks which Rooftop PV forecast run (from
ROOFTOP_PV_FCST_P5_RUN) was used for the Area in which 5-Minute Pre-dispatch run.
Column Schema
AREAID
Area identifier aligning with the load forecasting areas, tracks to ROOFTOP_PV_FCST_P5_RUN.AREAID.
SQL Type: VARCHAR2(10)
Polars Type: str
This column is mandatory, and a primary key.
RUN_DATETIME
Datetime (interval ending) of the 5-Minute Pre-dispatch run.
SQL Type: DATE
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Example: 2026/01/20 00:05:00
FORECAST_PRIORITY
Priority of the forecast run used for the DS run, tracks to ROOFTOP_PV_FCST_P5_RUN.FORECAST_PRIORITY.
SQL Type: NUMBER(10,0)
Polars Type: pl.Int64
This column is not mandatory.
FORECAST_RUN_DATETIME
Datetime (interval ending) when this forecast run is valid. It aligns with run_datetime, unless a forecast run is missed, in this case the previous run is used. Tracks to ROOFTOP_PV_FCST_P5_RUN.FORECAST_RUN_DATETIME.
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
OFFERDATETIME
Submission datetime of the forecast run used for the DS run, tracks to ROOFTOP_PV_FCST_P5_RUN.OFFERDATETIME.
SQL Type: DATE
Polars Type: pl.Datetime()
This column is not mandatory.
Example: 2026/01/20 00:05:00
PROVIDERID
Provider identifier of the forecast run used for the DS run, tracks to ROOFTOP_PV_FCST_P5_RUN.PROVIDERID.
SQL Type: VARCHAR2(20)
Polars Type: pl.Categorical
This column is not mandatory.
New Data Timing
Every 5 minutes when a new 5-Minute Pre-dispatch run is published
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", "FORECAST_RUN_DATETIME", "OFFERDATETIME").str.strptime(
pl.Datetime, dt_s_fmt
),
)
.cast(
{
"AREAID": str,
"FORECAST_PRIORITY": int, # or pl.Int64
"PROVIDERID": pl.Categorical,
}
)
# Deduplicate
# We want one row per ['AREAID', 'RUN_DATETIME']
.unique(subset=["AREAID", "RUN_DATETIME"], 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:
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.
| Report Name | Report Subtype | Version |
|---|---|---|
| P5MIN | ROOFTOP_PV_FCST_TRK | 1 |
This means that the CSV files start with metadata columns:
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: P5MIN