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Gulcihan Ozdemir

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Jul 2026

Learning-based probabilistic load forecasting with post-hoc and in-model uncertainty

This work develops a unified one-day-ahead probabilistic forecasting framework that aligns temporal resolution, reconstructs the unavailable inputs, and derives causal features, and compares a modular post-hoc residual-quantile scheme with an integrated in-model quantile-learning scheme.

S. Al-Shareeda, Gulcihan Ozdemir, H. Jeon · 0 citations