# 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 of 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, default None) – 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

1

M. Lave, J. Kleissl and J.S. Stein. A Wavelet-Based Variability Model (WVM) for Solar PV Power Plants. IEEE Transactions on Sustainable Energy, vol. 4, no. 2, pp. 501-509, 2013.

2

M. Lave and J. Kleissl. Cloud speed impact on solar variability scaling - Application to the wavelet variability model. Solar Energy, vol. 91, pp. 11-21, 2013.

3

Wavelet Variability Model - Matlab Code: https://github.com/sandialabs/wvm