Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.
Using bias-corrected reanalysis to simulate current and future wind power output
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.