| Title |
Debiased Active Learning for Sky-Image-Based PV Power Prediction |
| Authors |
신승협(Seung-Hyeop Shin) ; 김윤영(Yoon-Yeong Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.3.569 |
| Keywords |
Active Learning; Debiasing Matrix; Photovoltaic Power Prediction; Sky Image; Solar Forecasting |
| Abstract |
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. |