Several models have been developed to estimate the operating cell temperatures of photovoltaic (PV) modules because they directly affect the performance of each PV module. In this study, two prediction models used most commonly, the nominal operating cell temperature (NOCT) model and the Sandia National Laboratory temperature prediction model (SNL), were investigated for their suitability in the prediction of PV module's temperatures for building integrated photovoltaic (BIPV) installation in the tropical climate conditions of Thailand. It was found that, in general, the SNL model tends to give better results of temperature prediction than those of the NOCT model. Nevertheless, both models are strongly over-biased in temperature predictions. The discrepancies of the predictions are basically caused by the dissimilarity of the BIPV installation and the standard installation as specified by the models, rather than the effect of differences in climatic conditions between the temperate and tropical zones. In the worst case, it was found that the highest value of the mean bias error (MBE) is +8°C, or equivalent to +21% of the mean observed temperature, and the root mean square error (RMSE) is ±10°C, or equivalent to ±24% of the mean observed temperature. However, although these errors were large, their effects on the accuracy of the final prediction of the electrical power output generated by the PV module over a long term would not be great. The error of the expected generated energy output would not be more than 6% of the averaged actual energy output, which is acceptable for most applications.