Accurate hour-ahead forecasting of solar photovoltaic (PV) power is essential for risk-aware decision-making in power systems with increasing renewables. Although recent studies emphasize complex deep learning architectures, it remains unclear whether such complexity provides tangible benefits at very short forecasting horizons, particularly when forecast uncertainty is considered. This study evaluates deterministic and probabilistic hour-ahead PV forecasting using models of varying complexity, including persistence, linear autoregressive models with exogenous inputs, ridge regression, DLinear, and a vanilla long short-term memory (LSTM) network. Probabilistic forecasts were constructed using a unified, model-agnostic, adaptive conformal inference framework incorporating a daily miscoverage reset tailored to the diurnal characteristics of PV generation. Deterministic results indicate that the LSTM achieves the lowest errors, with an RMSE of 0.336 kW (6.55% of rated capacity) and an MAE of 0.164 kW, compared to RMSE values of approximately 0.38–0.45 kW for linear models and persistence. Following conformal calibration, all models attain empirical prediction interval coverage close to the nominal 90% level (PICP ≈ 90.8–91.4%), with performance differences reflected in interval width and sharpness rather than coverage. Notably, linear models combined with adaptive calibration deliver probabilistic performance comparable to the LSTM at substantially lower computational cost.
Vishnu Suresh (Tue,) studied this question.