短期极值风速概率预报的拼接Gamma-广义Pareto模型
A spliced Gamma-Generalized Pareto model for short-term extreme wind speed probabilistic forecasting
摘要：
风能等可再生能源已成为化石燃料能源的可持续替代能源。然而，风速的间歇性所带来的不确定性和波动性给风电生产的稳定性带来了很大的威胁，也给风电机组本身带来了很大的威胁。最近，在开发预测平均风速值的模型方面做了很多工作，但令人惊讶的是，很少有人专注于提出准确预测极端风速的模型，这可能会损坏涡轮机。在这项工作中，我们开发了一个灵活的拼接伽玛广义帕累托模型，同时预测极端和非极端风速。我们的模型属于隐高斯模型类，其推理是方便地进行集成嵌套拉普拉斯近似方法的基础上。考虑到一个灵活的加法回归结构，我们提出了两个模型的潜在线性预测捕捉风速的时空动态。我们的模型拟合速度快，可以描述风速分布的主体和尾部，同时产生短期极端和非极端风速概率预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
