energy_performance_indicator

energy_performance_indicator ¤

EnergyPerformanceIndicatorEvents ¤

EnergyPerformanceIndicatorEvents(
    dataframe: DataFrame,
    *,
    event_uuid: str = "energy:enpi",
    time_column: str = "systime",
    uuid_column: str = "uuid"
)

Bases: Base

Energy: Performance Indicator (EnPI) per ISO 50001

Calculate energy consumed per unit of production output (EnPI = kWh/unit). Tracks EnPI against a rolling baseline and identifies improvement/degradation trends. Supports comparison across multiple meters / production areas.

Supports two data models via constructor parameters:

Standard (ts-shape default)::

EnergyPerformanceIndicatorEvents(df)

expects: systime | uuid | value_double | value_integer¤
Raw CSV::

EnergyPerformanceIndicatorEvents(df, time_column="time", uuid_column="id")

Methods: - enpi_by_window: EnPI (energy / units) per time window. - enpi_vs_baseline: EnPI vs rolling baseline with anomaly flags. - enpi_by_hierarchy: EnPI across multiple meters for area comparison.

enpi_by_window ¤

enpi_by_window(
    meter_uuid: str,
    counter_uuid: str,
    *,
    energy_column: str = "value_double",
    counter_column: str = "value_integer",
    window: str = "1D"
) -> pd.DataFrame

Calculate energy per unit produced (EnPI) for each time window.

Parameters:

Name	Type	Description	Default
`meter_uuid`	`str`	Identifier of the energy meter signal.	required
`counter_uuid`	`str`	Identifier of the production counter signal.	required
`energy_column`	`str`	Column containing energy readings.	`'value_double'`
`counter_column`	`str`	Column containing counter readings.	`'value_integer'`
`window`	`str`	Aggregation window (e.g. '1D', '1h', '1W').	`'1D'`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	start, uuid, source_uuid, is_delta, energy_kwh, units_produced, enpi

enpi_vs_baseline ¤

enpi_vs_baseline(
    meter_uuid: str,
    counter_uuid: str,
    *,
    energy_column: str = "value_double",
    counter_column: str = "value_integer",
    window: str = "1D",
    baseline_window: int = 30,
    deviation_threshold: float = 0.1
) -> pd.DataFrame

Compare current EnPI against a rolling baseline.

Parameters:

Name	Type	Description	Default
`meter_uuid`	`str`	Identifier of the energy meter signal.	required
`counter_uuid`	`str`	Identifier of the production counter signal.	required
`energy_column`	`str`	Column containing energy readings.	`'value_double'`
`counter_column`	`str`	Column containing counter readings.	`'value_integer'`
`window`	`str`	Aggregation window.	`'1D'`
`baseline_window`	`int`	Number of windows for rolling baseline.	`30`
`deviation_threshold`	`float`	Fractional deviation to flag as anomaly (0.1 = 10%).	`0.1`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	start, uuid, source_uuid, enpi, baseline_enpi, deviation_pct, is_anomaly, trend

enpi_by_hierarchy ¤

enpi_by_hierarchy(
    meter_uuids: list[str],
    counter_uuid: str,
    *,
    energy_column: str = "value_double",
    counter_column: str = "value_integer",
    window: str = "1D"
) -> pd.DataFrame

Calculate EnPI per meter for cross-area comparison.

Useful for comparing energy intensity across production lines, buildings, or hierarchy levels. Combine with series metadata to map meter_uuid to label_lvl / hierarchy columns.

Parameters:

Name	Type	Description	Default
`meter_uuids`	`list[str]`	List of energy meter identifiers.	required
`counter_uuid`	`str`	Shared production counter identifier.	required
`energy_column`	`str`	Column containing energy readings.	`'value_double'`
`counter_column`	`str`	Column containing counter readings.	`'value_integer'`
`window`	`str`	Aggregation window.	`'1D'`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	start, meter_uuid, energy_kwh, units_produced, enpi

energy_performance_indicator