Work

Hidden Markov Models for Bounded, Inflated Time Series: Forecasting Icing on Wind Turbine Blades

Year: 2026

Type: article

Abstract: ABSTRACT Time series analysis of icing‐induced power loss in wind turbines pose several challenges: the response is bounded, serially dependent, intermittently missing, highly dispersed, and often inflated at a single value. We address these challenges with discrete‐time hidden Markov models for a discrete‐continuous process assumed to follow a mixture of state‐dependent zero‐inflated beta distributions. The framework allows covariates to influence either the transition probabilities or the distribution parameters. In a case study, we evaluate 12‐h‐ahead forecasts of icing‐related power loss. Compared with autoregressive and regression baselines, the proposed model achieves the highest predictive accuracy.

Source: Wind Energy

DOI: 10.1002/we.70110

Authors: Albert S. Bisgaard, Jan Kloppenborg Møller, Henrik Madsen, Tobias K. S. Ritschel

Institution: Technical University of Denmark

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,730