arxiv
PublishedOctober 3, 2026 at 4:00 AM
—neutral
Transfer Learning with Conformalized Quantile Regression for Solar PV Forecasting Under Load-Shedding-Driven Data Scarcity
Publisher summary· verbatim
arXiv:2609.26959v2 Announce Type: replace Abstract: Solar photovoltaic (PV) forecasting in regions affected by load shedding is challenging because reliable historical observations are scarce. This study proposes a transfer learning framework combined with Conformalized Quantile Regression (CQR) to
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivRCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods8harxivCan Graph Learning Learn Circuits?8harxivBeyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery8harxivTrajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification8hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗