Source code for energy_analysis_toolbox.timeseries.profiles.thresholds.relative_std
"""Module for computing threshold profiles based on relative standard deviations.
This module provides tools to calculate profiles that deviate from the mean profile
by a user-defined multiple of the standard deviation derived from historical data.
The profiles aim to capture variability while maintaining a consistent baseline.
"""
import pandas as pd
from energy_analysis_toolbox.timeseries.profiles.mean_profile import MeanProfile
[docs]
class RelativeSTDThreshold(MeanProfile):
"""A class which implements a statistical deviation from the mean profile."""
def __init__(
self,
offset_std: float = 3,
**kwargs,
) -> None:
"""Initialize RelativeSTDThreshold.
Parameters
----------
offset_std : float, optional
Number of standard deviations VS the computed reference to obtain
the threshold profile. Default is 3 (profile is 3 standard deviations
from reference)
"""
self.offset_std = offset_std
super().__init__(**kwargs)
[docs]
def compute(
self,
history: pd.Series,
time: pd.Timestamp,
**kwargs,
) -> pd.Series:
"""Return a threshold profile.
The threshold profile is obtained using a user-defined relative variation
from the mean profile built from history.
Parameters
----------
history : pd.Series
Consumption history used to computed the reference
profile.
time : pd.Timestamp
The time at which the profile is of interest. Only
the information about the date is used in the passed
timestamp.
Returns
-------
profile : pd.Series
Profile threshold with same resolution as the history data
Notes
-----
The profile threshold is obtained as the mean profile obtained
from history + ``tshd`` times the standard deviation profile.
"""
profile_groups = self.group(history)
reference = super().compute(history, time, **kwargs)
offset = self.offset_std * profile_groups.std()
offset.index = reference.index
return reference + offset
