pvlib.scaling.wvm

pvlib.scaling.wvm(clearsky_index, positions, cloud_speed, dt=None)

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 (np.array) – Array of shape (N, 2) of coordinates (x, y) representing the easting, northing in meters [m] of the PV systems. Large PV plants could be simulated by gridded points over the plant’s 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

[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: sandialabs/wvm