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.
Probabilistic modelling of wind turbine power curves with application of heteroscedastic gaussian process regression,
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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.