A spliced Gamma-Generalized Pareto model for short-term extreme wind speed probabilistic forecasting

TL;DR

This paper introduces a novel spliced Gamma-Generalized Pareto model for accurate short-term probabilistic forecasting of both extreme and non-extreme wind speeds, enhancing wind power stability and turbine safety.

Contribution

It proposes a flexible, fast-fitting spliced model within the latent Gaussian framework to simultaneously forecast the entire wind speed distribution, including extremes.

Findings

Model effectively captures wind speed distribution tails.

Provides accurate probabilistic forecasts for extreme winds.

Fast inference via integrated nested Laplace approximation.

Abstract

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 spliced Gamma-Generalized Pareto model to forecast extreme and non-extreme wind speeds simultaneously. Our model belongs to the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. Considering a flexible additive regression structure, we propose two models for the latent linear predictor to capture the spatio-temporal dynamics of wind speeds. 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.