Impact of Data Temporal Resolution on the Energy Yield Modeling of Tandem Photovoltaic Devices

Full physics‐based equivalent circuit models for tandem photovoltaic (PV) devices provide high accuracy but are computationally intensive. This study investigates reducing simulation runtime by downsampling meteorological input data using three methods—mean, median, and slicing—across two distinct climates (Colorado and Oregon) on the energy yield modeling of tandem devices. These methods represent both post‐measurement downsampling (mean and median) and changes in field measurement frequency (slicing). We show that the number of device model evaluations may be reduced by up to 99% while maintaining an annualized energy yield estimate error of less than 0.2% if the resampling method conserves total solar insolation. Conversely, the nonconservative slicing and median methods produce unreliable results with errors exceeding 5% due to inherent energy nonconservation. We identify two primary drivers of modeling error: energy conservation and PV nonlinearity. While these underlying error mechanisms are climate agnostic, the resultant impact on energy yield estimates varies in different meteorological regimes. In high‐volatility environments, smoothing effects suppress subcell mismatch and nonlinear losses, leading to overestimation. In low‐irradiance conditions, efficiency losses dominate, shifting errors toward underestimation. The results of this work provide a framework for high‐speed tandem modeling that balances computational efficiency with site‐specific accuracy.