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MTPASA_REGIONSUMMARY

Region Results summary over aggregation periods.

MTPASA_REGIONSUMMARY is public data.

Column Schema

AGGREGATION_PERIOD

Period data is aggregated over. Values are YEAR, MONTH

SQL Type: VARCHAR2(20) Polars Type: str

This column is mandatory, and a primary key.

Examples:

  • MONTH
  • YEAR

DEMAND_POE_TYPE

Demand POE type used. Value are POE10, POE50

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

This column is mandatory, and a primary key.

Examples:

  • POE10
  • POE50

PERIOD_ENDING

Datetime of day at end of period (i.e. last day of month or year reported)

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

This column is mandatory, and a primary key.

Example: 2026/10/01 00:00:00

REGIONID

The unique region identifier

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(20). This is actually a factor/enum, with values:

  • QLD1

  • VIC1

  • TAS1

  • NSW1

  • SA1

  • SNOWY1

This column is mandatory, and a primary key.

RUN_DATETIME

Date processing of the run begins.

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

This column is mandatory, and a primary key.

Example: 2026/09/07 01:00:00

RUN_NO

Unique run id.

SQL Type: NUMBER(4) Polars Type: pl.Int16

This column is mandatory, and a primary key.

Examples: 1177, 1184

RUNTYPE

Type of run. Always RELIABILITY

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

This column is mandatory, and a primary key.

Example: RELIABILITY

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: 2026/09/08 12:29:02

LRC

LRC Condition reported (Value=1) if USE_WEIGHTED_AVG >= 0.002% otherwise (Value=0)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0, 1

NATIVEDEMAND

Native demand calculated from Operational As Generated trace supplied by Energy Forecasting

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 117314.69, 869504, 72933777.98, 4547386

NUMBEROFITERATIONS

Total number of iterations and reference years performed

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 600

USE_AVERAGE

Average period unserved energy across iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.01, 0, 17, 3988.47

USE_EVENT_LOWERQUARTILE

Lower quartile unserved energy event size across all half hourly intervals and iterations and reference years that have unserved energy>0 (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 1.7, 0, 108, 221

USE_EVENT_MAX

Maximum unserved energy event size across all half hourly intervals and iterations and reference years that have unserved energy>0 (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 1.7, 0, 669, 4177.01

USE_EVENT_MEDIAN

Median unserved energy event size across all half hourly intervals and iterations and reference years that have unserved energy>0 (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 1.7, 0, 1329.21

USE_EVENT_MIN

Minimum unserved energy event size across all half hourly intervals and iterations and reference years that have unserved energy>0 (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.13, 0, 521.41

USE_EVENT_UPPERQUARTILE

Upper quartile unserved energy event size across all half hourly intervals and iterations and reference years that have unserved energy>0 (MW)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 1.7, 0, 1908.27

USE_NUMBEROFITERATIONS

Number of iterations and reference years showing unserved energy

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0, 1, 57

USE_PERCENTILE10

Unserved energy period amount at the 10th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE20

Unserved energy period amount at the 20th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE30

Unserved energy period amount at the 30th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE40

Unserved energy period amount at the 40th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE50

Unserved energy period amount at the 50th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE60

Unserved energy period amount at the 60th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE70

Unserved energy period amount at the 70th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE80

Unserved energy period amount at the 80th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE90

Unserved energy period amount at the 90th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

USE_PERCENTILE100

Unserved energy period amount at the 100th percentile of iterations and reference years (MWh)

SQL Type: NUMBER(12,2) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.85, 0, 580, 24241

USE_WEIGHTED_AVG

Weighted average USE per region = (USE_AVERAGE_POE10/NATIVE_DEMAND_POE_10WEIGHT_POE_10 + USE_AVERAGE_POE50/NATIVE_DEMAND_POE_50WEIGHT_POE_50)*100

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.000001, 0, 0.059534

WEIGHT

Fixed Values of 0.696 for 50 POE and 0.304 for 10 POE.

SQL Type: NUMBER(16,6) Polars Type: pl.Float64

This column is not mandatory.

Examples: 0.304, 0.392

New Data Timing

On MTPASA solution published to the market (usually once per week)

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("PERIOD_ENDING", "RUN_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "AGGREGATION_PERIOD": str,
            "DEMAND_POE_TYPE": pl.Categorical,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "RUN_NO": int,  # or pl.Int16
            "RUNTYPE": pl.Categorical,
            "LRC": pl.Float64,
            "NATIVEDEMAND": pl.Float64,
            "NUMBEROFITERATIONS": pl.Float64,
            "USE_AVERAGE": pl.Float64,
            "USE_EVENT_LOWERQUARTILE": pl.Float64,
            "USE_EVENT_MAX": pl.Float64,
            "USE_EVENT_MEDIAN": pl.Float64,
            "USE_EVENT_MIN": pl.Float64,
            "USE_EVENT_UPPERQUARTILE": pl.Float64,
            "USE_NUMBEROFITERATIONS": pl.Float64,
            "USE_PERCENTILE10": pl.Float64,
            "USE_PERCENTILE20": pl.Float64,
            "USE_PERCENTILE30": pl.Float64,
            "USE_PERCENTILE40": pl.Float64,
            "USE_PERCENTILE50": pl.Float64,
            "USE_PERCENTILE60": pl.Float64,
            "USE_PERCENTILE70": pl.Float64,
            "USE_PERCENTILE80": pl.Float64,
            "USE_PERCENTILE90": pl.Float64,
            "USE_PERCENTILE100": pl.Float64,
            "USE_WEIGHTED_AVG": pl.Float64,
            "WEIGHT": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['AGGREGATION_PERIOD', 'DEMAND_POE_TYPE', 'PERIOD_ENDING', 'REGIONID', 'RUN_DATETIME', 'RUN_NO', 'RUNTYPE']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=[
            "AGGREGATION_PERIOD",
            "DEMAND_POE_TYPE",
            "PERIOD_ENDING",
            "REGIONID",
            "RUN_DATETIME",
            "RUN_NO",
            "RUNTYPE",
        ],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_MTPASA_REGIONSUMMARY_FILE01_202609010000.zip

RUN_DATETIMERUN_NORUNTYPEDEMAND_POE_TYPEAGGREGATION_PERIODPERIOD_ENDINGREGIONIDNATIVEDEMANDUSE_PERCENTILE10USE_PERCENTILE20USE_PERCENTILE30USE_PERCENTILE40USE_PERCENTILE50USE_PERCENTILE60USE_PERCENTILE70USE_PERCENTILE80USE_PERCENTILE90USE_PERCENTILE100USE_AVERAGENUMBEROFITERATIONSUSE_NUMBEROFITERATIONSUSE_EVENT_MAXUSE_EVENT_UPPERQUARTILEUSE_EVENT_MEDIANUSE_EVENT_LOWERQUARTILEUSE_EVENT_MINWEIGHTUSE_WEIGHTED_AVGLRCLASTCHANGED
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00NSW13162254.30000000001497.362.56001869.97440.72278.77164.8868.360.3040.00006502026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00QLD12343112.26000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00SA1538083.75000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00TAS1533777.38000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00VIC12285613.84000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00NSW15243705.35000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00QLD14116980.05000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00SA1836824.46000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00TAS1869598.7400000000034.630.09600269.2562.8956.5350.1743.810.3040.00000702026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00VIC13640116.44000000000006000000000.304002026/09/08 12:29:02
2026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/12/01 00:00:00NSW15203462.96000000000006000000000.304002026/09/08 12:29:02
...

CSETP.WORLDDVD_MTPASA_REGIONSUMMARYAEMOPUBLIC2026/10/0915:56:00001791525360325MONTHLY_ARCHIVE001791525360325
IMTPASAREGIONSUMMARY1RUN_DATETIMERUN_NORUNTYPEDEMAND_POE_TYPEAGGREGATION_PERIODPERIOD_ENDINGREGIONIDNATIVEDEMANDUSE_PERCENTILE10USE_PERCENTILE20USE_PERCENTILE30USE_PERCENTILE40USE_PERCENTILE50USE_PERCENTILE60USE_PERCENTILE70USE_PERCENTILE80USE_PERCENTILE90USE_PERCENTILE100USE_AVERAGENUMBEROFITERATIONSUSE_NUMBEROFITERATIONSUSE_EVENT_MAXUSE_EVENT_UPPERQUARTILEUSE_EVENT_MEDIANUSE_EVENT_LOWERQUARTILEUSE_EVENT_MINWEIGHTUSE_WEIGHTED_AVGLRCLASTCHANGED
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00NSW13162254.30000000001497.362.56001869.97440.72278.77164.8868.360.3040.00006502026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00QLD12343112.26000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00SA1538083.75000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00TAS1533777.38000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/10/01 00:00:00VIC12285613.84000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00NSW15243705.35000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00QLD14116980.05000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00SA1836824.46000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00TAS1869598.7400000000034.630.09600269.2562.8956.5350.1743.810.3040.00000702026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/11/01 00:00:00VIC13640116.44000000000006000000000.304002026/09/08 12:29:02
DMTPASAREGIONSUMMARY12026/09/07 01:00:001177RELIABILITYPOE10MONTH2026/12/01 00:00:00NSW15203462.96000000000006000000000.304002026/09/08 12:29:02
...
CEND OF REPORT1003

RUN_DATETIME,RUN_NO,RUNTYPE,DEMAND_POE_TYPE,AGGREGATION_PERIOD,PERIOD_ENDING,REGIONID,NATIVEDEMAND,USE_PERCENTILE10,USE_PERCENTILE20,USE_PERCENTILE30,USE_PERCENTILE40,USE_PERCENTILE50,USE_PERCENTILE60,USE_PERCENTILE70,USE_PERCENTILE80,USE_PERCENTILE90,USE_PERCENTILE100,USE_AVERAGE,NUMBEROFITERATIONS,USE_NUMBEROFITERATIONS,USE_EVENT_MAX,USE_EVENT_UPPERQUARTILE,USE_EVENT_MEDIAN,USE_EVENT_LOWERQUARTILE,USE_EVENT_MIN,WEIGHT,USE_WEIGHTED_AVG,LRC,LASTCHANGED
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",NSW1,3162254.3,0,0,0,0,0,0,0,0,0,1497.36,2.5,600,1,869.97,440.72,278.77,164.88,68.36,0.304,0.000065,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",QLD1,2343112.26,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",SA1,538083.75,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",TAS1,533777.38,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",VIC1,2285613.84,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",NSW1,5243705.35,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",QLD1,4116980.05,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",SA1,836824.46,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",TAS1,869598.74,0,0,0,0,0,0,0,0,0,34.63,0.09,600,2,69.25,62.89,56.53,50.17,43.81,0.304,0.000007,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",VIC1,3640116.44,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/12/01 00:00:00",NSW1,5203462.96,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
...

C,SETP.WORLD,DVD_MTPASA_REGIONSUMMARY,AEMO,PUBLIC,2026/10/09,15:56:00,001791525360325,MONTHLY_ARCHIVE,001791525360325
I,MTPASA,REGIONSUMMARY,1,RUN_DATETIME,RUN_NO,RUNTYPE,DEMAND_POE_TYPE,AGGREGATION_PERIOD,PERIOD_ENDING,REGIONID,NATIVEDEMAND,USE_PERCENTILE10,USE_PERCENTILE20,USE_PERCENTILE30,USE_PERCENTILE40,USE_PERCENTILE50,USE_PERCENTILE60,USE_PERCENTILE70,USE_PERCENTILE80,USE_PERCENTILE90,USE_PERCENTILE100,USE_AVERAGE,NUMBEROFITERATIONS,USE_NUMBEROFITERATIONS,USE_EVENT_MAX,USE_EVENT_UPPERQUARTILE,USE_EVENT_MEDIAN,USE_EVENT_LOWERQUARTILE,USE_EVENT_MIN,WEIGHT,USE_WEIGHTED_AVG,LRC,LASTCHANGED
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",NSW1,3162254.3,0,0,0,0,0,0,0,0,0,1497.36,2.5,600,1,869.97,440.72,278.77,164.88,68.36,0.304,0.000065,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",QLD1,2343112.26,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",SA1,538083.75,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",TAS1,533777.38,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/10/01 00:00:00",VIC1,2285613.84,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",NSW1,5243705.35,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",QLD1,4116980.05,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",SA1,836824.46,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",TAS1,869598.74,0,0,0,0,0,0,0,0,0,34.63,0.09,600,2,69.25,62.89,56.53,50.17,43.81,0.304,0.000007,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/11/01 00:00:00",VIC1,3640116.44,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
D,MTPASA,REGIONSUMMARY,1,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,MONTH,"2026/12/01 00:00:00",NSW1,5203462.96,0,0,0,0,0,0,0,0,0,0,0,600,0,0,0,0,0,0,0.304,0,0,"2026/09/08 12:29:02"
...
C,"END OF REPORT",1003

Availability

Visibility: Public

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 5 of 2018. The most recent available data is month 9 of 2026.

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

Data Size

Each year of data is approximately 188 KB when zipped, 2 MB as unzipped CSV.

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
MTPASA REGIONSUMMARY 1

This means that the CSV files start with metadata columns:

I,MTPASA,REGIONSUMMARY,1,...
D,MTPASA,REGIONSUMMARY,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: MTPASA