Accurate hour-ahead photovoltaic (PV) forecasts are essential for grid balancing, intraday trading, and renewable integration. While Transformer architectures have recently reshaped time series forecasting, their application to short-term PV prediction with calibrated uncertainty remains largely unexplored. This study provides a systematic benchmark of five Transformer variants (Autoformer, Informer, FEDformer, DLinear, and PatchTST) evaluated on a five-year, rooftop PV dataset (5 kW peak) against an unseen 12-month test set. All models are trained within a pipeline using a 48-h rolling input window with cyclical temporal encodings to ensure comparability. Beyond point forecasts, we introduce Adaptive Conformal Inference (ACI), a distribution-free and adaptive framework, to quantify uncertainty in real time. The results demonstrate that PatchTST, through its patch-based temporal tokenization, delivers superior accuracy (MAE = 0.194 kW, RMSE = 0.381 kW), outperforming both classical persistence and other Transformer baselines. When coupled with ACI, PatchTST achieves 86.2% empirical coverage with narrow intervals (0.62 kW mean width) and probabilistic scores (CRPS = 0.54; Winkler = 1.86) that strike a balance between sharpness and reliability. The findings establish that combining patch-based Transformers with adaptive conformal calibration provides a novel and viable route to risk-aware PV forecasting.
Vishnu Suresh (2025) studied this question.