energy_analysis_toolbox.power.basics#

Applies elementary operations on power timeseries.

energy_analysis_toolbox.power.basics.integrate_over(intervals: DataFrame, power_series: Series) → Series[source]#

Return the energy integrated over slices of the power series.

Warning

This function only works on slices of the power series. It cannot be used to integrate the power with smaller granularity.

Parameters:

  • intervals (pd.DataFrame) – A table of overconsumption defined with ‘start’ and ‘end’ columns containing timestamps. These timestamps are used as slice bounds in the series with included start and excluded end.

  • power_series (pd.Series) – A timeseries of power in (W). The series must have at least two elements so that the timestep(s) can be deduced.

Returns:

energies (pd.Series) – A series of energy in (J) with same index as overconsumption.

Note

Proper integration with a resolution independent from the timestep can be achieved by transforming the power to energy using to_energy and then using the volume_conservative <energy_analysis_toolbox.timeseries.power.conservative.volume_conservative resampling.

Examples

>>> power = _constant_power(); power
2023-10-29 00:00:00+02:00    1
2023-10-29 01:00:00+02:00    1
...
2023-10-30 22:00:00+01:00    1
2023-10-30 23:00:00+01:00    1
Freq: H, dtype: object
>>> overconsumption = _intervals()
>>> overconsumption
                       start                        end
A  2023-10-29 00:00:00+02:00  2023-10-29 01:00:00+02:00
B  2023-10-30 02:00:00+02:00  2023-10-30 02:00:00+01:00
>>> eat.power.integrate_over(overconsumption, power)
A    3600.0
B    3600.0
dtype: float64

Remind that the interval bounds are used to slice the series (with excluded end). Accordingly, an interval is completely accounted for in the integration as soon as it falls with the slice : >>> overconsumption[‘end’] += pd.Timedelta(‘10min’) >>> eat.power.integrate_over(overconsumption, power) A 7200.0 B 7200.0 dtype: float64

energy_analysis_toolbox.power.basics.to_energy(power_series: Series) → Series[source]#

Return a series of energy per timestep.

Parameters:

power_series (pd.Series) – Timeseries of (avg) power per [ti, ti+1[ timestep in (W). The series must have at least two elements so that the timestep(s) can be deduced. The last timestep is assumed to be the same as the former last one.

Returns:

pd.Series – Timeseries of energy per timestep in (J) with same index as input series.