Accurate modeling of photovoltaic (PV) performance requires precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. This failure to account for thermal transients leads to temperature prediction inaccuracy at fine data intervals characterized by intermittent irradiance and wind. On the other hand, complex physics-based transient models that account for the thermal mass are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous time steps are weighted and averaged to accurately predict the module temperature at finer time scales. The parameters for this model are determined by detailed, three-dimensional finite element analyses that calculate the effect of wind speed and module unit mass on module temperature. These effects are represented as a weighted moving average that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements as high as 1.45°C. The model also significantly reduces the variability between the model predictions and measured temperature times series data.