- pvlib.temperature.prilliman(temp_cell, wind_speed, unit_mass=11.1, coefficients=None)[source]#
Smooth short-term cell temperature transients using the Prilliman model.
The Prilliman et al. model 1 applies a weighted moving average to the output of a steady-state cell temperature model to account for a module’s thermal inertia by smoothing the cell temperature’s response to changing weather conditions.
This implementation requires the time series inputs to be regularly sampled in time with frequency less than 20 minutes. Data with irregular time steps (including from data gaps, missing leap days, etc) should be resampled prior to using this function.
temp_cell (pandas.Series with DatetimeIndex) – Cell temperature modeled with steady-state assumptions. [C]
wind_speed (pandas.Series) – Wind speed, adjusted to correspond to array height [m/s]
unit_mass (float, default 11.1) – Total mass of module divided by its one-sided surface area [kg/m^2]
coefficients (4-element list-like, optional) – Values for coefficients a_0 through a_3, see Eq. 9 of 1
temp_cell (pandas.Series) – Smoothed version of the input cell temperature. Input temperature with sampling interval >= 20 minutes is returned unchanged. [C]
This smoothing model was developed and validated using the SAPM cell temperature model for the steady-state input.
Smoothing is done using the 20 minute window behind each temperature value. At the beginning of the series where a full 20 minute window is not possible, partial windows are used instead.
temp_cell[k]is NaN when input
wind_speed[k]is NaN, or when no non-NaN data are in the input temperature for the 20 minute window preceding index
M. Prilliman, J. S. Stein, D. Riley and G. Tamizhmani, “Transient Weighted Moving-Average Model of Photovoltaic Module Back-Surface Temperature,” IEEE Journal of Photovoltaics, 2020. DOI: 10.1109/JPHOTOV.2020.2992351