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EnergyConsumptionEvents¤

Analyze energy consumption patterns from meter/sensor signals.

Module: ts_shape.events.energy.consumption_analysis Guide: Production Guide

When to Use¤

Use for energy management and ISO 50001 compliance. Tracks consumption patterns, identifies peak demand periods, and calculates specific energy consumption per unit produced. Works with utility meter data, sub-meter readings, or any cumulative/instantaneous power signal.

Quick Example¤

from ts_shape.events.energy.consumption_analysis import EnergyConsumptionEvents

energy = EnergyConsumptionEvents(
    df=meter_df,
    timestamp_col="timestamp",
    value_col="power_kw"
)

# Aggregate consumption per shift
consumption = energy.consumption_by_window(window="8H", agg="sum")

# Find peak demand periods
peaks = energy.peak_demand_detection(window="15T", top_n=10)
print(f"Highest peak: {peaks.iloc[0]['peak_kw']:.1f} kW")

# Compare actual vs baseline consumption
deviations = energy.consumption_baseline_deviation(
    baseline_col="expected_kw", threshold_pct=15.0
)

# Energy per production unit (requires production count column)
epu = energy.energy_per_unit(production_col="units_produced", window="1D")

Key Methods¤

Method Purpose Returns
consumption_by_window() Aggregate consumption per time window DataFrame with total/mean consumption per window
peak_demand_detection() Identify peak demand periods DataFrame of top-N peak demand windows
consumption_baseline_deviation() Actual vs baseline comparison DataFrame of periods exceeding deviation threshold
energy_per_unit() Energy per production unit DataFrame with specific energy consumption per window

Tips & Notes¤

Align windows with shift boundaries

Use window sizes that match your operational shifts (e.g., "8H" for 8-hour shifts). Misaligned windows split consumption across shifts and obscure real patterns.

Related modules

See Also¤