A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
arXiv:2605.22390v1 Announce Type: new Abstract: Accurate wind power forecasting requires reliable uncertainty quantification, yet most existing methods report a single predictive uncertainty that conflates epistemic and aleatoric sources. This paper applies the law of total variance to the joint setting of heteroscedastic neural network regression and Bayesian posterior approximation, deriving an explicit decomposition of total uncertainty (TU) into aleatoric (AU) and epistemic (EU) components. The resulting estimators are compatible with standard posterior-approximation methods and with $\bet
The increasing sophistication of AI models and the growing demand for reliable real-world applications (like energy grid management) necessitate more robust uncertainty quantification methods.
Reliable uncertainty quantification in AI is critical for deploying autonomous systems in high-stakes environments, enabling more trustworthy and transparent decision-making.
This research provides a more granular understanding of model uncertainty, allowing for better identification and mitigation of risks in AI-driven predictions, particularly in contexts like renewable energy forecasting.
- · Renewable Energy Operators
- · AI/ML Developers
- · Risk Management Firms
- · Energy Grid Operators
- · AI Models with Opaque Uncertainty
- · Ad-hoc Risk Assessment Methods
Improved reliability and safety of AI-driven systems in critical infrastructure.
Accelerated adoption of AI in sectors requiring high precision and explainability, such as finance and autonomous vehicles.
Potential for new regulatory frameworks and industry standards emphasizing uncertainty quantification in AI deployments.
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Read at arXiv cs.LG