GENUNITMTRINPERIOD

GENUNITMTRINPERIOD shows meter reading by period for each generator meter. GENUNITMTRINPERIOD covers generated power flowing into the system. It is used to calculate settlement values.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

GENUNITMTRINPERIOD data is confidential to the relevant participant.

Source

GENUNITMTRINPERIOD updated only when new meter reading files are submitted by MDAs.

Column Schema

CONNECTIONPOINTID

Connection Point NMI

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

LOCAL_RETAILER

Local Retailer for this NMI

SQL Type: VARCHAR2(10) Polars Type: str

This column is mandatory, and a primary key.

MDA

Relevant Metering Data Agent

SQL Type: VARCHAR2(10) Polars Type: str

This column is mandatory, and a primary key.

PARTICIPANTID

Unique participant identifier

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

PERIODID

Period number where 1 period ending 00:30 EST

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

This column is mandatory, and a primary key.

SETTLEMENTDATE

Trading date of meter data submitted

Warning

Despite what the name suggests, this is a datetime, not a date.

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

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

VERSIONNO

Version no of the record for the given effective date

SQL Type: NUMBER(6,0) Polars Type: pl.UInt32

This column is mandatory, and a primary key.

EXPORTENERGYVALUE

Energy received from the pool (MWh)

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

This column is not mandatory.

EXPORTREACTIVEVALUE

Reactive power received from the network

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

This column is not mandatory.

GENUNITID

Physical unit ID

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

IMPORTENERGYVALUE

Energy sent to the pool (MWh)

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

This column is not mandatory.

IMPORTREACTIVEVALUE

Reactive power sent to the network

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

This column is not mandatory.

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

STATIONID

Station Identifier

For more information, see the dedicated documentation page.

SQL Type: VARCHAR2(10) Polars Type: pl.Categorical

This column is not mandatory.

New Data Timing

When new meter reading files are submitted by MDAs

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("SETTLEMENTDATE", "LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
    )
    .cast(
        {
            "CONNECTIONPOINTID": pl.Categorical,
            "LOCAL_RETAILER": str,
            "MDA": str,
            "PARTICIPANTID": pl.Categorical,
            "PERIODID": int,  # or pl.Int16
            "VERSIONNO": int,  # or pl.UInt32
            "EXPORTENERGYVALUE": pl.Float64,
            "EXPORTREACTIVEVALUE": pl.Float64,
            "GENUNITID": str,
            "IMPORTENERGYVALUE": pl.Float64,
            "IMPORTREACTIVEVALUE": pl.Float64,
            "STATIONID": pl.Categorical,
        }
    )
    # Deduplicate
    # We want one row per ['CONNECTIONPOINTID', 'LOCAL_RETAILER', 'MDA', 'PARTICIPANTID', 'PERIODID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(
        subset=[
            "CONNECTIONPOINTID",
            "LOCAL_RETAILER",
            "MDA",
            "PARTICIPANTID",
            "PERIODID",
            "SETTLEMENTDATE",
            "VERSIONNO",
        ],
        keep="last",
    )
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Deduplication and PDR Loader Configuration

The primary keys are:

CONNECTIONPOINTID
LOCAL_RETAILER
MDA
PARTICIPANTID
PERIODID
SETTLEMENTDATE
VERSIONNO

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

You should probably take the row with the maximum LASTCHANGED.