idle_energy_detection

idle_energy_detection ¤

IdleEnergyDetectionEvents ¤

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

Bases: Base

Energy: Idle Energy Detection

Cross-reference an energy meter signal with a boolean machine-state signal to detect and quantify energy consumed during idle (non-production) periods.

Supports two data models via constructor parameters:

Standard (ts-shape default)::

IdleEnergyDetectionEvents(df)

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

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

Methods: - idle_energy_by_window: Idle vs running energy per time window. - idle_energy_by_shift: Idle waste aggregated per shift. - idle_energy_trend: Rolling trend of idle energy waste.

idle_energy_by_window ¤

idle_energy_by_window(
    meter_uuid: str,
    state_uuid: str,
    *,
    energy_column: str = "value_double",
    state_column: str = "value_bool",
    window: str = "1h",
    idle_threshold: float = 0.1
) -> pd.DataFrame

Aggregate energy consumed during idle periods per time window.

Machine-running percentage is the mean of the boolean state signal within each window. Windows below idle_threshold are classified as idle.

Parameters:

Name	Type	Description	Default
`meter_uuid`	`str`	Identifier of the energy meter signal.	required
`state_uuid`	`str`	Identifier of the boolean machine-state signal (True=running).	required
`energy_column`	`str`	Column containing energy readings.	`'value_double'`
`state_column`	`str`	Column containing boolean state values.	`'value_bool'`
`window`	`str`	Resample window (e.g. '1h', '15min').	`'1h'`
`idle_threshold`	`float`	machine_running_pct below this classifies the window as idle.	`0.1`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	start, uuid, source_uuid, is_delta, total_energy, idle_energy, running_energy, machine_running_pct, idle_fraction

idle_energy_by_shift ¤

idle_energy_by_shift(
    meter_uuid: str,
    state_uuid: str,
    *,
    energy_column: str = "value_double",
    state_column: str = "value_bool",
    shift_definitions: (
        dict[str, tuple[str, str]] | None
    ) = None
) -> pd.DataFrame

Aggregate idle energy waste per shift across all dates.

Parameters:

Name	Type	Description	Default
`meter_uuid`	`str`	Identifier of the energy meter signal.	required
`state_uuid`	`str`	Identifier of the boolean machine-state signal.	required
`energy_column`	`str`	Column containing energy readings.	`'value_double'`
`state_column`	`str`	Column containing boolean state values.	`'value_bool'`
`shift_definitions`	`dict[str, tuple[str, str]] \| None`	Dict mapping shift name → (start_time, end_time). Default: three-shift operation (06-14, 14-22, 22-06).	`None`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	shift, total_energy, idle_energy, waste_fraction

idle_energy_trend ¤

idle_energy_trend(
    meter_uuid: str,
    state_uuid: str,
    *,
    energy_column: str = "value_double",
    state_column: str = "value_bool",
    window: str = "1D",
    trend_window: int = 7,
    idle_threshold: float = 0.1
) -> pd.DataFrame

Rolling trend of idle energy waste over time.

Parameters:

Name	Type	Description	Default
`meter_uuid`	`str`	Identifier of the energy meter signal.	required
`state_uuid`	`str`	Identifier of the boolean machine-state signal.	required
`energy_column`	`str`	Column containing energy readings.	`'value_double'`
`state_column`	`str`	Column containing boolean state values.	`'value_bool'`
`window`	`str`	Aggregation window (default daily).	`'1D'`
`trend_window`	`int`	Number of windows for rolling average.	`7`
`idle_threshold`	`float`	machine_running_pct below this → idle.	`0.1`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	start, uuid, source_uuid, idle_energy, rolling_avg_idle_energy, trend_direction

idle_energy_detection