Hidden Markov Models for Bounded, Inflated Time Series: Forecasting Icing on Wind Turbine Blades
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.
Fields of Study & Subjects
Icing and De-icing Technologies
Energy Load and Power Forecasting
Smart Materials for Construction
Publication details
- Journal / source
- Wind Energy
- Publisher
- Wiley
- Volume
- 29
- Issue
- 5
- Publication date
- 21 April 2026
- Language
- EN