A three-stage model for short-term extreme wind speed probabilistic forecasting
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Renewable sources of energy such as wind power have become a sustainable alternative to fossil fuel-based energy. However, the uncertainty and fluctuation of the wind speed derived from its intermittent nature bring a great threat to the wind power production stability, and to the wind turbines themselves. Lately, much work has been done on developing models to forecast average wind speed values, yet surprisingly little has focused on proposing models to accurately forecast extreme wind speeds, which can damage the turbines. In this work, we develop a flexible three-stage model to forecast extreme and non-extreme wind speeds simultaneously. Each stage of our model belongs to the class of latent Gaussian models, for which the integrated nested Laplace approximation method is designed. Considering a flexible additive regression structure, we propose two models for the latent linear predictor to capture the spatio-temporal dynamics of wind speeds. The first linear predictor is a temporal model that incorporates spatial information trough lagged off-site predictors, chosen as a function of the dominant wind directions. The second one uses stochastic partial differential approximations to the Matérn covariance of a Gaussian field that varies in time according to a first-order autoregressive process. Our models are fast to fit and can describe both the bulk and the tail of the wind speed distribution while producing short-term extreme and non-extreme wind speed probabilistic forecasts.
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