pvlib.scaling.wvm#
- pvlib.scaling.wvm(clearsky_index, positions, cloud_speed, dt=None)[source]#
Compute spatial aggregation time series smoothing on clear sky index based on the Wavelet Variability model.
This model is described in Lave et al. [1], [2]. Implementation is basically a port of the Matlab version of the code [3].
- Parameters:
clearsky_index (numeric or pandas.Series) – Clear Sky Index time series that will be smoothed.
positions (numeric) – Array of coordinate distances as (x,y) pairs representing the easting, northing of the site positions in meters [m]. Distributed plants could be simulated by gridded points throughout the plant footprint.
cloud_speed (numeric) – Speed of cloud movement in meters per second [m/s].
dt (float, optional) – The time series time delta. By default, is inferred from the clearsky_index. Must be specified for a time series that doesn’t include an index. Units of seconds [s].
- Returns:
smoothed (numeric or pandas.Series) – The Clear Sky Index time series smoothed for the described plant.
wavelet (numeric) – The individual wavelets for the time series before smoothing.
tmscales (numeric) – The timescales associated with the wavelets in seconds [s].
References