DEMANDOPERATIONALACTUAL
Shows Actual Operational Demand for a particular date time interval.
Column Schema
INTERVAL_DATETIME
Date time interval for operational demand value
SQL Type: DATE
Polars Type: pl.Datetime()
This column is mandatory, and a primary key.
Example: 2026/09/01 00:30:00
REGIONID
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.
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: 2026/09/01 00:30:02
OPERATIONAL_DEMAND
Average 30-minute measured operational demand MW value (unadjusted)
SQL Type: NUMBER(10,0)
Polars Type: pl.Int64
This column is not mandatory.
Examples:
774, 8935
OPERATIONAL_DEMAND_ADJUSTMENT
Adjustment value containing the estimated amount of activated RERT and involuntary load shedding that occurred as a result of a NER 4.8.9 instruction for load shedding from AEMO.
SQL Type: NUMBER(10,0)
Polars Type: pl.Int64
This column is not mandatory.
Example: 0
WDR_ESTIMATE
Estimated average 30-minute MW amount of Wholesale Demand Response that occurred
SQL Type: NUMBER(10)
Polars Type: pl.Int64
This column is not mandatory.
Example: 0
New Data Timing
Ad hoc
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",
)
.rename(
{
# These columns have names that conflict with metadata columns
"OPERATIONAL_DEMAND_duplicated_0": "OPERATIONAL_DEMAND"
}
)
.with_columns(
pl.col("INTERVAL_DATETIME", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
)
.cast(
{
"REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
"OPERATIONAL_DEMAND": int, # or pl.Int64
"OPERATIONAL_DEMAND_ADJUSTMENT": int, # or pl.Int64
"WDR_ESTIMATE": int, # or pl.Int64
}
)
# Deduplicate
# We want one row per ['INTERVAL_DATETIME', 'REGIONID']
# Tie-breaker: greatest value of ['INTERVAL_DATETIME', 'REGIONID']
.sort(["INTERVAL_DATETIME", "REGIONID"], descending=False)
.unique(subset=["INTERVAL_DATETIME", "REGIONID"], keep="last")
)
Nemosis
This table is not yet available in Nemosis.
Example File
From PUBLIC_ARCHIVE_DEMANDOPERATIONALACTUAL_FILE01_202609010000.zip
| INTERVAL_DATETIME | REGIONID | OPERATIONAL_DEMAND | LASTCHANGED | OPERATIONAL_DEMAND_ADJUSTMENT | WDR_ESTIMATE |
| 2026/09/01 00:30:00 | NSW1 | 7834 | 2026/09/01 00:30:02 | 0 | 0 |
| 2026/09/01 00:30:00 | QLD1 | 5806 | 2026/09/01 00:30:02 | 0 | 0 |
| 2026/09/01 00:30:00 | SA1 | 1435 | 2026/09/01 00:30:02 | 0 | 0 |
| 2026/09/01 00:30:00 | TAS1 | 992 | 2026/09/01 00:30:02 | 0 | 0 |
| 2026/09/01 00:30:00 | VIC1 | 5631 | 2026/09/01 00:30:02 | 0 | 0 |
| 2026/09/01 01:00:00 | TAS1 | 968 | 2026/09/01 01:00:01 | 0 | 0 |
| 2026/09/01 01:00:00 | NSW1 | 7684 | 2026/09/01 01:00:01 | 0 | 0 |
| 2026/09/01 01:00:00 | QLD1 | 5652 | 2026/09/01 01:00:01 | 0 | 0 |
| 2026/09/01 01:00:00 | SA1 | 1444 | 2026/09/01 01:00:01 | 0 | 0 |
| 2026/09/01 01:00:00 | VIC1 | 5465 | 2026/09/01 01:00:01 | 0 | 0 |
| 2026/09/01 01:30:00 | NSW1 | 7458 | 2026/09/01 01:30:01 | 0 | 0 |
| ... |
| C | SETP.WORLD | DVD_DEMANDOPERATIONALACTUAL | AEMO | PUBLIC | 2026/10/09 | 15:25:03 | 001791523503750 | MONTHLY_ARCHIVE | 001791523503750 |
| I | OPERATIONAL_DEMAND | ACTUAL | 3 | INTERVAL_DATETIME | REGIONID | OPERATIONAL_DEMAND | LASTCHANGED | OPERATIONAL_DEMAND_ADJUSTMENT | WDR_ESTIMATE |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 00:30:00 | NSW1 | 7834 | 2026/09/01 00:30:02 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 00:30:00 | QLD1 | 5806 | 2026/09/01 00:30:02 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 00:30:00 | SA1 | 1435 | 2026/09/01 00:30:02 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 00:30:00 | TAS1 | 992 | 2026/09/01 00:30:02 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 00:30:00 | VIC1 | 5631 | 2026/09/01 00:30:02 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 01:00:00 | TAS1 | 968 | 2026/09/01 01:00:01 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 01:00:00 | NSW1 | 7684 | 2026/09/01 01:00:01 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 01:00:00 | QLD1 | 5652 | 2026/09/01 01:00:01 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 01:00:00 | SA1 | 1444 | 2026/09/01 01:00:01 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 01:00:00 | VIC1 | 5465 | 2026/09/01 01:00:01 | 0 | 0 |
| D | OPERATIONAL_DEMAND | ACTUAL | 3 | 2026/09/01 01:30:00 | NSW1 | 7458 | 2026/09/01 01:30:01 | 0 | 0 |
| ... | |||||||||
| C | END OF REPORT | 1003 |
INTERVAL_DATETIME,REGIONID,OPERATIONAL_DEMAND,LASTCHANGED,OPERATIONAL_DEMAND_ADJUSTMENT,WDR_ESTIMATE
"2026/09/01 00:30:00",NSW1,7834,"2026/09/01 00:30:02",0,0
"2026/09/01 00:30:00",QLD1,5806,"2026/09/01 00:30:02",0,0
"2026/09/01 00:30:00",SA1,1435,"2026/09/01 00:30:02",0,0
"2026/09/01 00:30:00",TAS1,992,"2026/09/01 00:30:02",0,0
"2026/09/01 00:30:00",VIC1,5631,"2026/09/01 00:30:02",0,0
"2026/09/01 01:00:00",TAS1,968,"2026/09/01 01:00:01",0,0
"2026/09/01 01:00:00",NSW1,7684,"2026/09/01 01:00:01",0,0
"2026/09/01 01:00:00",QLD1,5652,"2026/09/01 01:00:01",0,0
"2026/09/01 01:00:00",SA1,1444,"2026/09/01 01:00:01",0,0
"2026/09/01 01:00:00",VIC1,5465,"2026/09/01 01:00:01",0,0
"2026/09/01 01:30:00",NSW1,7458,"2026/09/01 01:30:01",0,0
...
C,SETP.WORLD,DVD_DEMANDOPERATIONALACTUAL,AEMO,PUBLIC,2026/10/09,15:25:03,001791523503750,MONTHLY_ARCHIVE,001791523503750
I,OPERATIONAL_DEMAND,ACTUAL,3,INTERVAL_DATETIME,REGIONID,OPERATIONAL_DEMAND,LASTCHANGED,OPERATIONAL_DEMAND_ADJUSTMENT,WDR_ESTIMATE
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 00:30:00",NSW1,7834,"2026/09/01 00:30:02",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 00:30:00",QLD1,5806,"2026/09/01 00:30:02",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 00:30:00",SA1,1435,"2026/09/01 00:30:02",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 00:30:00",TAS1,992,"2026/09/01 00:30:02",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 00:30:00",VIC1,5631,"2026/09/01 00:30:02",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 01:00:00",TAS1,968,"2026/09/01 01:00:01",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 01:00:00",NSW1,7684,"2026/09/01 01:00:01",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 01:00:00",QLD1,5652,"2026/09/01 01:00:01",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 01:00:00",SA1,1444,"2026/09/01 01:00:01",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 01:00:00",VIC1,5465,"2026/09/01 01:00:01",0,0
D,OPERATIONAL_DEMAND,ACTUAL,3,"2026/09/01 01:30:00",NSW1,7458,"2026/09/01 01:30:01",0,0
...
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 8 of 2024. The most recent available data is month 9 of 2026.
Every month in between has a file. (No gaps.)
Warning
The REPORT_NAME metadata column has the same column name as an actual columns OPERATIONAL_DEMAND. So the raw CSVs have duplicate column names. You will need to be careful when reading this CSV.
Warning
The REPORT_NAME metadata column has the same column name as an actual columns OPERATIONAL_DEMAND. So the raw CSVs have duplicate column names. You will need to be careful when reading this CSV.
Data Size
Each year of data is approximately 558 KB when zipped, 6 MB as unzipped CSV.
Deduplication and PDR Loader Configuration
The primary keys are:
The transaction type is "INSERT-UPDATE", which means that you will likely see duplicate data with different values.
You should deduplicate by sorting based on LASTCHANGED, within each group of primary keys.
| Report Name | Report Subtype | Version |
|---|---|---|
| OPERATIONAL_DEMAND | ACTUAL | 2 |
| OPERATIONAL_DEMAND | ACTUAL | 3 |
This means that the CSV files start with metadata columns:
or
ROW_FILTER_TYPE is LASTCHANGED
Indexes
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