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

Investigating methods to improve photovoltaic thermal models at second-to-minute timescales