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Hidden Markov Models for Bounded, Inflated Time Series: Forecasting Icing on Wind Turbine Blades
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DOI：10.1002/we.70110.png)
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Abstract
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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.
Keywords:
ice accretion
latent mixture models
power loss
probabilistic forecast
wind power
zero-inflated beta distribution
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