A non-parametric adaptive conformal inference based probabilistic hour-ahead solar PV power forecasting method

Abstract

Accurate probabilistic solar photovoltaic (PV) power forecasting is essential for the reliable integration of solar energy into modern power grids. This study evaluates four uncertainty quantification methods for short-term PV forecasting: Adaptive Conformal Inference (ACI), Deep Quantile Regression (DQR), Bayesian Long Short-Term Memory (BLSTM), and CatBoost quantile regression. ACI is applied as a post-processing technique that adaptively adjusts prediction intervals based on recent forecast errors. We propose a novel modification to ACI in which the miscoverage parameter is reset at the start of each day to prevent the accumulation of calibration errors during nighttime periods when PV output is zero. This reset addresses the interval inflation commonly observed in standard ACI under strong diurnal variability, leading to more stable and reliable prediction intervals. Using a five-year dataset from Wroclaw University of Science and Technology, the modified ACI achieves the highest coverage (90.96%) with a mean interval width of 12.8% of peak power. BLSTM performs comparably with 83.32% coverage and 13.74% width. CatBoost yields the sharpest intervals (11.2%) but lower coverage (81.07%), while DQR provides the lowest coverage (79.48%) and the highest Winkler score. Although tested on a single site, the data-driven, model-agnostic nature of ACI supports generalization, and its independence from weather forecasts ensures robustness.