Debiased Active Learning for Sky-Image-Based PV Power Prediction

This study proposes a novel Debiased Active Learning (DAL) approach for solar photovoltaic (PV) power prediction based on ground-based sky images. Conventional uncertainty-based sampling methods often suffer from selection bias, particularly under class-imbalanced conditions where clear-sky samples dominate over cloudy-sky samples. To address this issue, the proposed DAL method estimates a debiasing matrix from a small, trusted validation dataset and uses it to correct the model’s predictive probabilities before uncertainty sampling. Using the SKIPP’D (Sky Images and Photovoltaic Power Dataset) dataset(≈300,000 paired sky images and PV power values), the proposed DAL significantly improves labeling efficiency and prediction accuracy compared to traditional uncertainty-based active learning approaches.