solarspatialtools.stats.variability_score

solarspatialtools.stats.variability_score(series, tau=1, moving_avg=True, pct=False)

Compute the variability score as proposed by Lave et al. [1]. The Variability Score is computed by calculating the product of each ramp rate that occurs in the time series and the probability of occurrence of larger ramp rates than that ramp rate of interest. The maximum value of that product over all possible ramp rates is the Variability Score. Its value may be represented as a percent.

The original source computes this quantity using the GHI, but we offer the possibility of computing it for clear sky index.

Parameters

seriespandas.Series or pandas.DataFrame

a time series for which to calculate the Variability Score. VS will be calculated along axis 0.

taunumeric, default 1

The number of timesteps for the increment calculation. series must use a temporal index to use a dt greater than 1.

moving_avgbool, default True

When tau specified with a value different from 1, should the timeseries be resampled via moving avg to the frequency of tau for computation?

pctbool, default False

should we scale the score as a percent of 1000 W/m2 (0-100%)?

Returns

variability_scorenumeric

the variability score

[1] M. Lave, M. J. Reno, and R. J. Broderick, “Characterizing local high- frequency solar variability and its impact to distribution studies,” Solar Energy 118, 327–337 (2015). https://www.osti.gov/pages/biblio/1497655