  This paper presents a range of methods to improve the accuracy of
equation-based thermal models of PV modules at second-to-minute timescales. We
present an RC-equivalent conceptual model for PV modules, where wind effects
are captured. We show how the thermal time constant $\tau$ of PV modules can be
determined from measured data, and subsequently used to make static thermal
models dynamic by applying the Exponential Weighted Mean (EWM) approach to
irradiance and wind signals. On average, $\tau$ is $6.3 \pm 1~$min for
fixed-mount PV systems. Based on this conceptual model, the Filter- EWM - Mean
Bias Error correction (FEM) methodology is developed. We propose two thermal
models, WM1 and WM2, and compare these against the models of Ross, Sandia, and
Faiman on twenty-four datasets of fifteen sites, with time resolutions ranging
from 1$~$s to 1$~$h, the majority of these at 1$~$min resolution. The FEM
methodology is shown to reduce model errors (RMSE and MAE) on average for all
sites and models versus the standard steady-state equivalent by -1.1$~$K and
-0.75$~$K respectively.
