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FPP_UNIT_MW

This report delivers the curated 4 second measurement MW data for each FPP unit

Column Schema

FPP_UNITID

FPP Unit ID (registered DUID/TNI)

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

Examples:

  • ADPBA1
  • ADPPV1
  • AGLHAL
  • AGLSOM
  • ANGAST1
  • APD_LOAD
  • ARWF1
  • AVLSF1
  • BALB1
  • BALDHWF1

INTERVAL_DATETIME

Date and time of the trading interval (DD/MM/YYYY HH24:MI:SS) fixed to the UTC+10 time zone (NEM time)

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

This column is mandatory, and a primary key.

Example: 2025/01/07 18:10:00

MEASUREMENT_DATETIME

Date and time of the SCADA data (DD/MM/YYYY HH24:MI:SS) fixed to the UTC+10 time zone (NEM time)

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

This column is mandatory, and a primary key.

Example: 2025/01/07 18:06:00

VERSIONNO

Version (FPP run number from the FPP database)

SQL Type: NUMBER(5) Polars Type: pl.UInt32

This column is mandatory, and a primary key.

Example: 1

DEVIATION_MW

Unit Deviation (output of the FPP calculation process)

SQL Type: NUMBER(18,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.00047, 0.00001, 0, 8

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: 2026/01/20 00:05:00

MEASURED_MW

Measured MW (4 second SCADA measurement in MW)

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

This column is not mandatory.

Examples: -0.003, 0.0000065, 0, 349

MW_QUALITY_FLAG

MW Quality (4 second SCADA measurement Quality) Supported values are: 0 = Bad Quality 1 = Good Quality 2 = Suspect Quality -1 = Value not used as input in the FPP calculation (generally used to indicate when SCADA data was late arriving after the FPP calculation commenced)

SQL Type: NUMBER(5) Polars Type: pl.Int32

This column is not mandatory.

Examples: 0, 1, 2

PARTICIPANTID

Participant ID

For more information, see the dedicated documentation page.

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

This column is not mandatory.

Examples:

  • AGLWBESS
  • ALCOAPA
  • ALNTARES
  • ALNTBHWF
  • ARARATWF
  • B2PS
  • BALBESS
  • BANGOWF2
  • BBPEM
  • BERBANK

SCHEDULED_MW

Scheduled MW (reference trajectory value from FPP calculation process)

SQL Type: NUMBER(18,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.01, 0.00067, 0, 685

New Data Timing

The private report delivers at the completion of an FPP calculation run for a closed trading interval (i.e. every 5 minutes)The public report delivers the next market day (at 4 am)

Code Examples

Polars

This table is so large that you should explicitly enumerate and cast every column, which will speed things up and prevents some errors. Although some of the datatypes are inferred, so they may be wrong. In particular, UInt16 vs UInt8 vs Int16 etc. If you get errors, you can swap these for just int.

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", "MEASUREMENT_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "FPP_UNITID": str,
            "VERSIONNO": pl.UInt32,
            "DEVIATION_MW": pl.Float64,
            "MEASURED_MW": pl.Float64,
            "MW_QUALITY_FLAG": pl.Int32,
            "PARTICIPANTID": pl.Categorical,
            "SCHEDULED_MW": pl.Float64,
        }
    )
    # Deduplicate
    # These files are so large that this step may cause your laptop to run out of memory.
    # We want one row per ['FPP_UNITID', 'INTERVAL_DATETIME', 'MEASUREMENT_DATETIME', 'VERSIONNO']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=["FPP_UNITID", "INTERVAL_DATETIME", "MEASUREMENT_DATETIME", "VERSIONNO"],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_FPP_UNIT_MW_FILE01_202412010000.zip

INTERVAL_DATETIMEMEASUREMENT_DATETIMEFPP_UNITIDVERSIONNOMEASURED_MWMW_QUALITY_FLAGSCHEDULED_MWDEVIATION_MWPARTICIPANTID
2025/01/07 18:10:002025/01/07 18:10:00CLEMGPWF133.71238136.75828-3.0459CLEMGPWF
2025/01/07 18:10:002025/01/07 18:10:00CLERMSF1118117.818590.18141CLERMSF
2025/01/07 18:10:002025/01/07 18:10:00CLOVER1-0.011-0.010SOUTHHYD
2025/01/07 18:10:002025/01/07 18:10:00CLRKCWF110.80137610.470.33138STANCCWF
2025/01/07 18:10:002025/01/07 18:10:00CLUNY12.1904325512.28565-0.09522HYDROTAS
2025/01/07 18:10:002025/01/07 18:10:00CNUNDAWF141.1480026140.0471.101CNUNDAWF
2025/01/07 18:10:002025/01/07 18:10:00COHUNSF110100ENELAUS
2025/01/07 18:10:002025/01/07 18:10:00COLEASF1158.89792159.25454-0.35662COLEAMSF
2025/01/07 18:10:002025/01/07 18:10:00COLUMSF1126.0763264126.101-0.02467COLUMSF
2025/01/07 18:10:002025/01/07 18:10:00COLWF011197.3693541187.58029.78915COLLECWF
2025/01/07 18:10:002025/01/07 18:10:00COOPGWF1157.11153.87893.2311COOPGWF
...

CSETP.WORLDDVD_FPP_UNIT_MWAEMOPUBLIC2025/01/1016:41:36001736491296087MONTHLY_ARCHIVE001736491296087
IFPPUNIT_MW1INTERVAL_DATETIMEMEASUREMENT_DATETIMEFPP_UNITIDVERSIONNOMEASURED_MWMW_QUALITY_FLAGSCHEDULED_MWDEVIATION_MWPARTICIPANTID
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00CLEMGPWF133.71238136.75828-3.0459CLEMGPWF
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00CLERMSF1118117.818590.18141CLERMSF
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00CLOVER1-0.011-0.010SOUTHHYD
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00CLRKCWF110.80137610.470.33138STANCCWF
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00CLUNY12.1904325512.28565-0.09522HYDROTAS
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00CNUNDAWF141.1480026140.0471.101CNUNDAWF
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00COHUNSF110100ENELAUS
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00COLEASF1158.89792159.25454-0.35662COLEAMSF
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00COLUMSF1126.0763264126.101-0.02467COLUMSF
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00COLWF011197.3693541187.58029.78915COLLECWF
DFPPUNIT_MW12025/01/07 18:10:002025/01/07 18:10:00COOPGWF1157.11153.87893.2311COOPGWF
...
CEND OF REPORT1003

INTERVAL_DATETIME,MEASUREMENT_DATETIME,FPP_UNITID,VERSIONNO,MEASURED_MW,MW_QUALITY_FLAG,SCHEDULED_MW,DEVIATION_MW,PARTICIPANTID
"2025/01/07 18:10:00","2025/01/07 18:10:00",CLEMGPWF,1,33.71238,1,36.75828,-3.0459,CLEMGPWF
"2025/01/07 18:10:00","2025/01/07 18:10:00",CLERMSF1,1,18,1,17.81859,0.18141,CLERMSF
"2025/01/07 18:10:00","2025/01/07 18:10:00",CLOVER,1,-0.01,1,-0.01,0,SOUTHHYD
"2025/01/07 18:10:00","2025/01/07 18:10:00",CLRKCWF1,1,0.801376,1,0.47,0.33138,STANCCWF
"2025/01/07 18:10:00","2025/01/07 18:10:00",CLUNY,1,2.19043255,1,2.28565,-0.09522,HYDROTAS
"2025/01/07 18:10:00","2025/01/07 18:10:00",CNUNDAWF,1,41.1480026,1,40.047,1.101,CNUNDAWF
"2025/01/07 18:10:00","2025/01/07 18:10:00",COHUNSF1,1,0,1,0,0,ENELAUS
"2025/01/07 18:10:00","2025/01/07 18:10:00",COLEASF1,1,58.89792,1,59.25454,-0.35662,COLEAMSF
"2025/01/07 18:10:00","2025/01/07 18:10:00",COLUMSF1,1,26.0763264,1,26.101,-0.02467,COLUMSF
"2025/01/07 18:10:00","2025/01/07 18:10:00",COLWF01,1,197.369354,1,187.5802,9.78915,COLLECWF
"2025/01/07 18:10:00","2025/01/07 18:10:00",COOPGWF1,1,57.11,1,53.8789,3.2311,COOPGWF
...

C,SETP.WORLD,DVD_FPP_UNIT_MW,AEMO,PUBLIC,2025/01/10,16:41:36,001736491296087,MONTHLY_ARCHIVE,001736491296087
I,FPP,UNIT_MW,1,INTERVAL_DATETIME,MEASUREMENT_DATETIME,FPP_UNITID,VERSIONNO,MEASURED_MW,MW_QUALITY_FLAG,SCHEDULED_MW,DEVIATION_MW,PARTICIPANTID
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",CLEMGPWF,1,33.71238,1,36.75828,-3.0459,CLEMGPWF
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",CLERMSF1,1,18,1,17.81859,0.18141,CLERMSF
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",CLOVER,1,-0.01,1,-0.01,0,SOUTHHYD
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",CLRKCWF1,1,0.801376,1,0.47,0.33138,STANCCWF
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",CLUNY,1,2.19043255,1,2.28565,-0.09522,HYDROTAS
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",CNUNDAWF,1,41.1480026,1,40.047,1.101,CNUNDAWF
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",COHUNSF1,1,0,1,0,0,ENELAUS
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",COLEASF1,1,58.89792,1,59.25454,-0.35662,COLEAMSF
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",COLUMSF1,1,26.0763264,1,26.101,-0.02467,COLUMSF
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",COLWF01,1,197.369354,1,187.5802,9.78915,COLLECWF
D,FPP,UNIT_MW,1,"2025/01/07 18:10:00","2025/01/07 18:10:00",COOPGWF1,1,57.11,1,53.8789,3.2311,COOPGWF
...
C,"END OF REPORT",1003

Availability

Visibility: Private & Public Next-Day

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 12 of 2024. The most recent available data is month 12 of 2024.

Every month in between has a file. (No gaps.)

Data Size

Each year of data is approximately 1 GB when zipped, 13 GB as unzipped CSV.

Tip

This table is large. Simply calling pd.read_csv() might fill up all the memory on your laptop. (As a rule of thumb, you need double the amount of memory as the size of the CSV.) You will need to think hard about memory optimisation. e.g. consider using Polars instead of Pandas, and selecting only the columns you care about.

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.

PDR Loader configuration is:

Report Name Report Subtype Version
FPP UNIT_MW 1

This means that the CSV files start with metadata columns:

I,FPP,UNIT_MW,1,...
D,FPP,UNIT_MW,1,...

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: FPP