MTPASA_REGIONRESULT

Region results for interval of max demand per day.

MTPASA_REGIONRESULT is public data.

Column Schema

DAY

Day this result is for

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

This column is mandatory, and a primary key.

Example: 2026/09/13 00:00:00

DEMAND_POE_TYPE

Demand POE type used. Value is POE10

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

This column is mandatory, and a primary key.

Example: POE10

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.

Example: 1177

RUNTYPE

Type of run. Always RELIABILITY

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

This column is mandatory, and a primary key.

Example: RELIABILITY

AGGREGATEINSTALLEDCAPACITY

The total installed capacity of all generation (MW)

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

This column is not mandatory.

Examples: 3362, 25976

DEMAND

Demand value from selected half hourly interval (MW)

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

This column is not mandatory.

Examples: 1138.75, 1165, 10997.68, 10218

DEMANDSIDEPARTICIPATION10

The 10th percentile for demand side participation, across iterations and reference years (MW)

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

This column is not mandatory.

Example: 0

DEMANDSIDEPARTICIPATION50

The 50th percentile for demand side participation, across iterations and reference years (MW)

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

This column is not mandatory.

Example: 0

DEMANDSIDEPARTICIPATION90

The 90th percentile for demand side participation, across iterations and reference years (MW)

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

This column is not mandatory.

Example: 0

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

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

PERIODID

Half hourly period reported, selected as period of maximum NEM scheduled demand (calculated as maximum of scheduled demands, averaged across iterations and reference years)

SQL Type: NUMBER(3,0) Polars Type: pl.Int16

This column is not mandatory.

Examples: 37, 40

TOTALAVAILABLEGEN10

The 10% percentile for available capacity, across iterations and reference years (MW).

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

This column is not mandatory.

Examples: 1435.48, 1517, 11870.6, 10914

TOTALAVAILABLEGEN50

The 50% percentile for available capacity, across iterations and reference years (MW).

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

This column is not mandatory.

Examples: 1589.48, 1594, 12669.23, 12308

TOTALAVAILABLEGEN90

The 90% percentile for available capacity, across iterations and reference years (MW).

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

This column is not mandatory.

Examples: 1686.98, 1730, 13289.03, 12009

TOTALAVAILABLEGENMAX

Maximum available capacity, across iterations and reference years (MW).

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

This column is not mandatory.

Examples: 1813.09, 1717, 13654.21, 13663

TOTALAVAILABLEGENMIN

Minimum available capacity, across iterations and reference years (MW).

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

This column is not mandatory.

Examples: 1091.96, 1183, 10884.1, 10007

TOTALINTERMITTENTGEN10

The 10th percentile for intermittent generation, across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 10.14, 58, 1708.42, 1392

TOTALINTERMITTENTGEN50

The 50th percentile for intermittent generation, across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 106.65, 140, 2673.14, 2099

TOTALINTERMITTENTGEN90

The 90th percentile for intermittent generation, across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 233.34, 448, 4587.8, 4242

TOTALSCHEDULEDGEN10

The 10th percentile for scheduled generation across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 321.03, 0, 328, 6106

TOTALSCHEDULEDGEN50

The 50th percentile for scheduled generation across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 3.29, 0, 497, 7416

TOTALSCHEDULEDGEN90

The 90th percentile for scheduled generation across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 7.41, 0, 175, 7630

TOTALSEMISCHEDULEGEN10

The 10% percentile for semi-scheduled generation across iterations and reference years (MW)

Warning

In the CSV files, this column actually has header TOTALSEMISCHEDULEDGEN10, not TOTALSEMISCHEDULEGEN10. In a SQL database, this should appear as TOTALSEMISCHEDULEGEN10

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

This column is not mandatory.

Examples: 6.41, 14, 1631.88, 514

TOTALSEMISCHEDULEGEN50

The 50% percentile for semi-scheduled generation across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 71.32, 152, 2542.13, 2236

TOTALSEMISCHEDULEGEN90

The 90% percentile for semi-scheduled generation across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 174.97, 306, 4328.96, 3253

USE_AVERAGE

Average unserved energy, across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 0.73, 0, 1.57

USE_EVENT_AVERAGE

Average unserved energy event size, across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 440.72, 0, 472.26

USE_LOWERQUARTILE

Lower quartile unserved energy, across iterations and reference years (MW)

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

This column is not mandatory.

Example: 0

USE_MAX

Maximum unserved energy, across iterations and reference years (MW)

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

This column is not mandatory.

Examples: 440.72, 0, 561.13

USE_MEDIAN

Median unserved energy, across iterations and reference years (MW)

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

This column is not mandatory.

Example: 0

USE_MIN

Minimum unserved energy, across iterations and reference years (MW)

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

This column is not mandatory.

Example: 0

USE_NUMBEROFITERATIONS

Number of iterations and reference years with unserved energy>0

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

This column is not mandatory.

Examples: 0, 1, 2

USE_UPPERQUARTILE

Upper quartile unserved energy, across iterations and reference years (MW)

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

This column is not mandatory.

Example: 0

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",
    )
    # These columns have names in the CSV which do not match the official schema
    .rename({"TOTALSEMISCHEDULEDGEN10": "TOTALSEMISCHEDULEGEN10"})
    .with_columns(
        pl.col("DAY", "RUN_DATETIME", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "DEMAND_POE_TYPE": pl.Categorical,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "RUN_NO": int,  # or pl.Int16
            "RUNTYPE": pl.Categorical,
            "AGGREGATEINSTALLEDCAPACITY": pl.Float64,
            "DEMAND": pl.Float64,
            "DEMANDSIDEPARTICIPATION10": pl.Float64,
            "DEMANDSIDEPARTICIPATION50": pl.Float64,
            "DEMANDSIDEPARTICIPATION90": pl.Float64,
            "NUMBEROFITERATIONS": pl.Float64,
            "PERIODID": int,  # or pl.Int16
            "TOTALAVAILABLEGEN10": pl.Float64,
            "TOTALAVAILABLEGEN50": pl.Float64,
            "TOTALAVAILABLEGEN90": pl.Float64,
            "TOTALAVAILABLEGENMAX": pl.Float64,
            "TOTALAVAILABLEGENMIN": pl.Float64,
            "TOTALINTERMITTENTGEN10": pl.Float64,
            "TOTALINTERMITTENTGEN50": pl.Float64,
            "TOTALINTERMITTENTGEN90": pl.Float64,
            "TOTALSCHEDULEDGEN10": pl.Float64,
            "TOTALSCHEDULEDGEN50": pl.Float64,
            "TOTALSCHEDULEDGEN90": pl.Float64,
            "TOTALSEMISCHEDULEGEN10": pl.Float64,
            "TOTALSEMISCHEDULEGEN50": pl.Float64,
            "TOTALSEMISCHEDULEGEN90": pl.Float64,
            "USE_AVERAGE": pl.Float64,
            "USE_EVENT_AVERAGE": pl.Float64,
            "USE_LOWERQUARTILE": pl.Float64,
            "USE_MAX": pl.Float64,
            "USE_MEDIAN": pl.Float64,
            "USE_MIN": pl.Float64,
            "USE_NUMBEROFITERATIONS": pl.Float64,
            "USE_UPPERQUARTILE": pl.Float64,
        }
    )
    # Deduplicate
    # We want one row per ['DAY', 'DEMAND_POE_TYPE', 'REGIONID', 'RUN_DATETIME', 'RUN_NO', 'RUNTYPE']
    # Tie-breaker: greatest value of LASTCHANGED
    .sort(LASTCHANGED, descending=False)
    .unique(
        subset=[
            "DAY",
            "DEMAND_POE_TYPE",
            "REGIONID",
            "RUN_DATETIME",
            "RUN_NO",
            "RUNTYPE",
        ],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_MTPASA_REGIONRESULT_FILE01_202609010000.zip

Table without MetadataTable with MetadataRaw Text without Metadata

RUN_DATETIME	RUN_NO	RUNTYPE	DEMAND_POE_TYPE	DAY	REGIONID	PERIODID	DEMAND	AGGREGATEINSTALLEDCAPACITY	NUMBEROFITERATIONS	USE_NUMBEROFITERATIONS	USE_MAX	USE_UPPERQUARTILE	USE_MEDIAN	USE_LOWERQUARTILE	USE_MIN	USE_AVERAGE	USE_EVENT_AVERAGE	TOTALSCHEDULEDGEN90	TOTALSCHEDULEDGEN50	TOTALSCHEDULEDGEN10	TOTALINTERMITTENTGEN90	TOTALINTERMITTENTGEN50	TOTALINTERMITTENTGEN10	DEMANDSIDEPARTICIPATION90	DEMANDSIDEPARTICIPATION50	DEMANDSIDEPARTICIPATION10	LASTCHANGED	TOTALSEMISCHEDULEGEN90	TOTALSEMISCHEDULEGEN50	TOTALSEMISCHEDULEGEN10	TOTALAVAILABLEGENMIN	TOTALAVAILABLEGEN10	TOTALAVAILABLEGEN50	TOTALAVAILABLEGEN90	TOTALAVAILABLEGENMAX
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	NSW1	37	9659.85	25963	600	0	0	0	0	0	0	0	0	8261.96	7082.85	5984.18	1467.55	579.14	147.46	0	0	0	2026/09/08 12:29:02	1418.86	540.92	144.05	8225.1	9266.91	10088.7	10575.76	10854
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	QLD1	37	7564.86	22725	600	0	0	0	0	0	0	0	0	6917.48	6194.1	5344.15	1339.39	749.68	480.67	0	0	0	2026/09/08 12:29:02	1339.39	749.68	480.67	7453.25	8207.73	8781.9	9282.46	9585
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	SA1	37	1667.02	7502	600	0	0	0	0	0	0	0	0	977.73	366.51	0	2008.98	867.45	234.16	0	0	0	2026/09/08 12:29:02	2008.98	867.45	234.16	1361	1793.51	1954.15	2043.85	2114.89
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	TAS1	37	1387.75	3362	600	0	0	0	0	0	0	0	0	1607.22	1348.77	880.14	497.62	307.87	117.04	0	0	0	2026/09/08 12:29:02	377.69	229.55	85.56	1316.51	1598.75	1778.75	1904.49	1940
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	VIC1	37	6881.12	20094	600	0	0	0	0	0	0	0	0	5369.59	4281.65	1549.26	3883.18	2194.05	224.85	0	0	0	2026/09/08 12:29:02	3664.61	2097.04	205.79	5259.63	6737.09	7293	7648.05	7839.33
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	NSW1	37	9578.79	25941	600	0	0	0	0	0	0	0	0	7521.3	6439.52	5644.55	1853.03	807.09	409.63	0	0	0	2026/09/08 12:29:02	1702.74	763.46	347.5	7960.64	9049.7	9820.83	10374.5	10640
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	QLD1	37	7535.65	22725	600	0	0	0	0	0	0	0	0	6893.99	6200.12	5408.59	1477.56	750.29	504.64	0	0	0	2026/09/08 12:29:02	1477.56	750.29	504.64	6921.27	8202.4	8745.66	9191.49	9492.02
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	SA1	37	1714.93	7502	600	0	0	0	0	0	0	0	0	1057.14	497	0	1985.33	1088.18	308.04	0	0	0	2026/09/08 12:29:02	1985.33	1088.18	308.04	1321.93	1786.45	1944.24	2037.04	2073.36
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	TAS1	37	1367.54	3362	600	0	0	0	0	0	0	0	0	1618.97	1241.08	862.95	485.11	266.49	104.05	0	0	0	2026/09/08 12:29:02	365.39	193.84	75.75	1205.71	1601.17	1790.32	1904.75	1940
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	VIC1	37	6887.83	20294	600	0	0	0	0	0	0	0	0	5352.59	3698.45	1595.84	4075.55	2260.1	717.11	0	0	0	2026/09/08 12:29:02	3820.27	2149.59	587.95	5429.97	6661.63	7268.62	7591.17	7813.79
2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/15 00:00:00	NSW1	37	9612.58	25941	600	0	0	0	0	0	0	0	0	7775.69	6544.53	5528.19	1829.53	755.84	258.72	0	0	0	2026/09/08 12:29:02	1669.26	704.95	253.83	7635.95	9103.41	9835.63	10372.31	10649
...

C	SETP.WORLD	DVD_MTPASA_REGIONRESULT	AEMO	PUBLIC	2026/10/09	15:55:58	001791525358258	MONTHLY_ARCHIVE	001791525358258
I	MTPASA	REGIONRESULT	2	RUN_DATETIME	RUN_NO	RUNTYPE	DEMAND_POE_TYPE	DAY	REGIONID	PERIODID	DEMAND	AGGREGATEINSTALLEDCAPACITY	NUMBEROFITERATIONS	USE_NUMBEROFITERATIONS	USE_MAX	USE_UPPERQUARTILE	USE_MEDIAN	USE_LOWERQUARTILE	USE_MIN	USE_AVERAGE	USE_EVENT_AVERAGE	TOTALSCHEDULEDGEN90	TOTALSCHEDULEDGEN50	TOTALSCHEDULEDGEN10	TOTALINTERMITTENTGEN90	TOTALINTERMITTENTGEN50	TOTALINTERMITTENTGEN10	DEMANDSIDEPARTICIPATION90	DEMANDSIDEPARTICIPATION50	DEMANDSIDEPARTICIPATION10	LASTCHANGED	TOTALSEMISCHEDULEGEN90	TOTALSEMISCHEDULEGEN50	TOTALSEMISCHEDULEGEN10	TOTALAVAILABLEGENMIN	TOTALAVAILABLEGEN10	TOTALAVAILABLEGEN50	TOTALAVAILABLEGEN90	TOTALAVAILABLEGENMAX
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	NSW1	37	9659.85	25963	600	0	0	0	0	0	0	0	0	8261.96	7082.85	5984.18	1467.55	579.14	147.46	0	0	0	2026/09/08 12:29:02	1418.86	540.92	144.05	8225.1	9266.91	10088.7	10575.76	10854
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	QLD1	37	7564.86	22725	600	0	0	0	0	0	0	0	0	6917.48	6194.1	5344.15	1339.39	749.68	480.67	0	0	0	2026/09/08 12:29:02	1339.39	749.68	480.67	7453.25	8207.73	8781.9	9282.46	9585
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	SA1	37	1667.02	7502	600	0	0	0	0	0	0	0	0	977.73	366.51	0	2008.98	867.45	234.16	0	0	0	2026/09/08 12:29:02	2008.98	867.45	234.16	1361	1793.51	1954.15	2043.85	2114.89
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	TAS1	37	1387.75	3362	600	0	0	0	0	0	0	0	0	1607.22	1348.77	880.14	497.62	307.87	117.04	0	0	0	2026/09/08 12:29:02	377.69	229.55	85.56	1316.51	1598.75	1778.75	1904.49	1940
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/13 00:00:00	VIC1	37	6881.12	20094	600	0	0	0	0	0	0	0	0	5369.59	4281.65	1549.26	3883.18	2194.05	224.85	0	0	0	2026/09/08 12:29:02	3664.61	2097.04	205.79	5259.63	6737.09	7293	7648.05	7839.33
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	NSW1	37	9578.79	25941	600	0	0	0	0	0	0	0	0	7521.3	6439.52	5644.55	1853.03	807.09	409.63	0	0	0	2026/09/08 12:29:02	1702.74	763.46	347.5	7960.64	9049.7	9820.83	10374.5	10640
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	QLD1	37	7535.65	22725	600	0	0	0	0	0	0	0	0	6893.99	6200.12	5408.59	1477.56	750.29	504.64	0	0	0	2026/09/08 12:29:02	1477.56	750.29	504.64	6921.27	8202.4	8745.66	9191.49	9492.02
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	SA1	37	1714.93	7502	600	0	0	0	0	0	0	0	0	1057.14	497	0	1985.33	1088.18	308.04	0	0	0	2026/09/08 12:29:02	1985.33	1088.18	308.04	1321.93	1786.45	1944.24	2037.04	2073.36
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	TAS1	37	1367.54	3362	600	0	0	0	0	0	0	0	0	1618.97	1241.08	862.95	485.11	266.49	104.05	0	0	0	2026/09/08 12:29:02	365.39	193.84	75.75	1205.71	1601.17	1790.32	1904.75	1940
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/14 00:00:00	VIC1	37	6887.83	20294	600	0	0	0	0	0	0	0	0	5352.59	3698.45	1595.84	4075.55	2260.1	717.11	0	0	0	2026/09/08 12:29:02	3820.27	2149.59	587.95	5429.97	6661.63	7268.62	7591.17	7813.79
D	MTPASA	REGIONRESULT	2	2026/09/07 01:00:00	1177	RELIABILITY	POE10	2026/09/15 00:00:00	NSW1	37	9612.58	25941	600	0	0	0	0	0	0	0	0	7775.69	6544.53	5528.19	1829.53	755.84	258.72	0	0	0	2026/09/08 12:29:02	1669.26	704.95	253.83	7635.95	9103.41	9835.63	10372.31	10649
...
C	END OF REPORT	1003

RUN_DATETIME,RUN_NO,RUNTYPE,DEMAND_POE_TYPE,DAY,REGIONID,PERIODID,DEMAND,AGGREGATEINSTALLEDCAPACITY,NUMBEROFITERATIONS,USE_NUMBEROFITERATIONS,USE_MAX,USE_UPPERQUARTILE,USE_MEDIAN,USE_LOWERQUARTILE,USE_MIN,USE_AVERAGE,USE_EVENT_AVERAGE,TOTALSCHEDULEDGEN90,TOTALSCHEDULEDGEN50,TOTALSCHEDULEDGEN10,TOTALINTERMITTENTGEN90,TOTALINTERMITTENTGEN50,TOTALINTERMITTENTGEN10,DEMANDSIDEPARTICIPATION90,DEMANDSIDEPARTICIPATION50,DEMANDSIDEPARTICIPATION10,LASTCHANGED,TOTALSEMISCHEDULEGEN90,TOTALSEMISCHEDULEGEN50,TOTALSEMISCHEDULEGEN10,TOTALAVAILABLEGENMIN,TOTALAVAILABLEGEN10,TOTALAVAILABLEGEN50,TOTALAVAILABLEGEN90,TOTALAVAILABLEGENMAX
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",NSW1,37,9659.85,25963,600,0,0,0,0,0,0,0,0,8261.96,7082.85,5984.18,1467.55,579.14,147.46,0,0,0,"2026/09/08 12:29:02",1418.86,540.92,144.05,8225.1,9266.91,10088.7,10575.76,10854
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",QLD1,37,7564.86,22725,600,0,0,0,0,0,0,0,0,6917.48,6194.1,5344.15,1339.39,749.68,480.67,0,0,0,"2026/09/08 12:29:02",1339.39,749.68,480.67,7453.25,8207.73,8781.9,9282.46,9585
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",SA1,37,1667.02,7502,600,0,0,0,0,0,0,0,0,977.73,366.51,0,2008.98,867.45,234.16,0,0,0,"2026/09/08 12:29:02",2008.98,867.45,234.16,1361,1793.51,1954.15,2043.85,2114.89
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",TAS1,37,1387.75,3362,600,0,0,0,0,0,0,0,0,1607.22,1348.77,880.14,497.62,307.87,117.04,0,0,0,"2026/09/08 12:29:02",377.69,229.55,85.56,1316.51,1598.75,1778.75,1904.49,1940
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",VIC1,37,6881.12,20094,600,0,0,0,0,0,0,0,0,5369.59,4281.65,1549.26,3883.18,2194.05,224.85,0,0,0,"2026/09/08 12:29:02",3664.61,2097.04,205.79,5259.63,6737.09,7293,7648.05,7839.33
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",NSW1,37,9578.79,25941,600,0,0,0,0,0,0,0,0,7521.3,6439.52,5644.55,1853.03,807.09,409.63,0,0,0,"2026/09/08 12:29:02",1702.74,763.46,347.5,7960.64,9049.7,9820.83,10374.5,10640
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",QLD1,37,7535.65,22725,600,0,0,0,0,0,0,0,0,6893.99,6200.12,5408.59,1477.56,750.29,504.64,0,0,0,"2026/09/08 12:29:02",1477.56,750.29,504.64,6921.27,8202.4,8745.66,9191.49,9492.02
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",SA1,37,1714.93,7502,600,0,0,0,0,0,0,0,0,1057.14,497,0,1985.33,1088.18,308.04,0,0,0,"2026/09/08 12:29:02",1985.33,1088.18,308.04,1321.93,1786.45,1944.24,2037.04,2073.36
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",TAS1,37,1367.54,3362,600,0,0,0,0,0,0,0,0,1618.97,1241.08,862.95,485.11,266.49,104.05,0,0,0,"2026/09/08 12:29:02",365.39,193.84,75.75,1205.71,1601.17,1790.32,1904.75,1940
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",VIC1,37,6887.83,20294,600,0,0,0,0,0,0,0,0,5352.59,3698.45,1595.84,4075.55,2260.1,717.11,0,0,0,"2026/09/08 12:29:02",3820.27,2149.59,587.95,5429.97,6661.63,7268.62,7591.17,7813.79
"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/15 00:00:00",NSW1,37,9612.58,25941,600,0,0,0,0,0,0,0,0,7775.69,6544.53,5528.19,1829.53,755.84,258.72,0,0,0,"2026/09/08 12:29:02",1669.26,704.95,253.83,7635.95,9103.41,9835.63,10372.31,10649
...

Raw Text with Metadata

C,SETP.WORLD,DVD_MTPASA_REGIONRESULT,AEMO,PUBLIC,2026/10/09,15:55:58,001791525358258,MONTHLY_ARCHIVE,001791525358258
I,MTPASA,REGIONRESULT,2,RUN_DATETIME,RUN_NO,RUNTYPE,DEMAND_POE_TYPE,DAY,REGIONID,PERIODID,DEMAND,AGGREGATEINSTALLEDCAPACITY,NUMBEROFITERATIONS,USE_NUMBEROFITERATIONS,USE_MAX,USE_UPPERQUARTILE,USE_MEDIAN,USE_LOWERQUARTILE,USE_MIN,USE_AVERAGE,USE_EVENT_AVERAGE,TOTALSCHEDULEDGEN90,TOTALSCHEDULEDGEN50,TOTALSCHEDULEDGEN10,TOTALINTERMITTENTGEN90,TOTALINTERMITTENTGEN50,TOTALINTERMITTENTGEN10,DEMANDSIDEPARTICIPATION90,DEMANDSIDEPARTICIPATION50,DEMANDSIDEPARTICIPATION10,LASTCHANGED,TOTALSEMISCHEDULEGEN90,TOTALSEMISCHEDULEGEN50,TOTALSEMISCHEDULEGEN10,TOTALAVAILABLEGENMIN,TOTALAVAILABLEGEN10,TOTALAVAILABLEGEN50,TOTALAVAILABLEGEN90,TOTALAVAILABLEGENMAX
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",NSW1,37,9659.85,25963,600,0,0,0,0,0,0,0,0,8261.96,7082.85,5984.18,1467.55,579.14,147.46,0,0,0,"2026/09/08 12:29:02",1418.86,540.92,144.05,8225.1,9266.91,10088.7,10575.76,10854
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",QLD1,37,7564.86,22725,600,0,0,0,0,0,0,0,0,6917.48,6194.1,5344.15,1339.39,749.68,480.67,0,0,0,"2026/09/08 12:29:02",1339.39,749.68,480.67,7453.25,8207.73,8781.9,9282.46,9585
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",SA1,37,1667.02,7502,600,0,0,0,0,0,0,0,0,977.73,366.51,0,2008.98,867.45,234.16,0,0,0,"2026/09/08 12:29:02",2008.98,867.45,234.16,1361,1793.51,1954.15,2043.85,2114.89
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",TAS1,37,1387.75,3362,600,0,0,0,0,0,0,0,0,1607.22,1348.77,880.14,497.62,307.87,117.04,0,0,0,"2026/09/08 12:29:02",377.69,229.55,85.56,1316.51,1598.75,1778.75,1904.49,1940
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/13 00:00:00",VIC1,37,6881.12,20094,600,0,0,0,0,0,0,0,0,5369.59,4281.65,1549.26,3883.18,2194.05,224.85,0,0,0,"2026/09/08 12:29:02",3664.61,2097.04,205.79,5259.63,6737.09,7293,7648.05,7839.33
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",NSW1,37,9578.79,25941,600,0,0,0,0,0,0,0,0,7521.3,6439.52,5644.55,1853.03,807.09,409.63,0,0,0,"2026/09/08 12:29:02",1702.74,763.46,347.5,7960.64,9049.7,9820.83,10374.5,10640
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",QLD1,37,7535.65,22725,600,0,0,0,0,0,0,0,0,6893.99,6200.12,5408.59,1477.56,750.29,504.64,0,0,0,"2026/09/08 12:29:02",1477.56,750.29,504.64,6921.27,8202.4,8745.66,9191.49,9492.02
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",SA1,37,1714.93,7502,600,0,0,0,0,0,0,0,0,1057.14,497,0,1985.33,1088.18,308.04,0,0,0,"2026/09/08 12:29:02",1985.33,1088.18,308.04,1321.93,1786.45,1944.24,2037.04,2073.36
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",TAS1,37,1367.54,3362,600,0,0,0,0,0,0,0,0,1618.97,1241.08,862.95,485.11,266.49,104.05,0,0,0,"2026/09/08 12:29:02",365.39,193.84,75.75,1205.71,1601.17,1790.32,1904.75,1940
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/14 00:00:00",VIC1,37,6887.83,20294,600,0,0,0,0,0,0,0,0,5352.59,3698.45,1595.84,4075.55,2260.1,717.11,0,0,0,"2026/09/08 12:29:02",3820.27,2149.59,587.95,5429.97,6661.63,7268.62,7591.17,7813.79
D,MTPASA,REGIONRESULT,2,"2026/09/07 01:00:00",1177,RELIABILITY,POE10,"2026/09/15 00:00:00",NSW1,37,9612.58,25941,600,0,0,0,0,0,0,0,0,7775.69,6544.53,5528.19,1829.53,755.84,258.72,0,0,0,"2026/09/08 12:29:02",1669.26,704.95,253.83,7635.95,9103.41,9835.63,10372.31,10649
...
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 10 MB when zipped, 101 MB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

DAY
DEMAND_POE_TYPE
REGIONID
RUN_DATETIME
RUN_NO
RUNTYPE

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	REGIONRESULT	1
MTPASA	REGIONRESULT	2

This means that the CSV files start with metadata columns:

I,MTPASA,REGIONRESULT,1,...
D,MTPASA,REGIONRESULT,1,...

or

I,MTPASA,REGIONRESULT,2,...
D,MTPASA,REGIONRESULT,2,...

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: MTPASA