On the Sufficiency of Direct Regression for Perovskite Solar Cell Degradation Forecasting

Accurate prediction of the long-term MPPT degradation trajectory of perovskite solar cells (PSCs) from short-term measurements can significantly reduce the time required for material characterization. Although conditional diffusion models have recently been introduced for degradation prediction in energy devices, their applicability to PSC-specific maximum power point tracking (MPPT) degradation trajectory forecasting remains uncertain due to the complexity of the underlying dynamics. This study benchmarks three approaches using 2245 devices from a publicly available dataset: NHITS, a hierarchical multilayer perceptron (MLP) with direct multi-horizon regression; Probabilistic NHITS (P-NHITS), which utilizes the same architecture with multi-quantile output; and TimeDiff, a conditional diffusion model with a CSDI backbone, autoregressive initialization, mode conditioning, and classifier-free guidance. The results indicate that PSC degradation under controlled conditions is predominantly single-exponential, with device-specific decay rates identifiable within the first 30 h. Therefore, the forecasting task is most appropriately framed as a regression problem rather than a generative one. NHITS achieves a root mean squared error (RMSE) of 0.738 PCE% compared to TimeDiff’s 0.863 (a 17% increase, $p<{10}^{-15}$), despite TimeDiff incorporating all architectural advantages reported in the literature. P-NHITS matches deterministic accuracy (0.744 PCE%) while providing 77% coverage prediction intervals without sampling, which is closer to the nominal 80% target than TimeDiff’s 63% coverage from 50 DDPM samples. For T90 (the time at which PCE first falls below 90% of its reference value) lifetime prediction restricted to forecast-window crossings, NHITS achieves a mean absolute error (MAE) of 16.2 h, outperforming TimeDiff’s 22.5 h. For smooth, unimodal degradation processes, direct regression with quantile outputs is both sufficient and preferable to conditional diffusion. Model selection should be guided by the underlying physical processes rather than by methodological trends.