Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty
Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs. Missing features must then be reconstructed, and their errors can propagate through the model. If this input uncertainty is not reflected, prediction intervals may become miscalibrated, affecting demand-response scheduling. Our work examines where uncertainty should be placed once inference inputs are reconstructed. We develop a unified one-day-ahead probabilistic forecasting framework that aligns temporal resolution, reconstruct
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- LinkedLinked via arxiv author · 85%Sarah Al-Shareeda →
“Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty”
- LinkedLinked via arxiv author · 85%Gulcihan Ozdemir →
“Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty”
- LinkedLinked via arxiv author · 85%Heung Seok Jeon →
“Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty”
- FuzzySimilar title/name (fuzzy) · 59%sktime/pytorch-forecasting →
“Fuzzy title match (0.73): “Learning-based Probabilistic Load Forecasting with Post-hoc ” ≈ “sktime/pytorch-forecasting””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Learning-based Probabilistic Load Forecasting with Post-hoc ” ≈ “aymericdamien/TopDeepLearning””