论文标题
使用非二元坡道功能和深度学习模型将风变异性与模拟风向斜向事件进行建模
Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models
论文作者
论文摘要
在风力输出中，大型坡道被称为坡道事件的预测对于将大量的风能纳入国家电网至关重要。风能供应的巨大变化必须由辅助能源补偿，包括使用化石燃料。改善风力的预测将有助于减少对补充能源的依赖，以及它们的相关成本和排放。在本文中，我们讨论了当前预测实践的局限性，并探讨了使用机器学习方法来增强风力坡道事件的分类和预测。另外，我们概述了一种新颖的风坡道预测方法的设计，其中高分辨率风场与风能建模合并。
The forecasting of large ramps in wind power output known as ramp events is crucial for the incorporation of large volumes of wind energy into national electricity grids. Large variations in wind power supply must be compensated by ancillary energy sources which can include the use of fossil fuels. Improved prediction of wind power will help to reduce dependency on supplemental energy sources along with their associated costs and emissions. In this paper, we discuss limitations of current predictive practices and explore the use of Machine Learning methods to enhance wind ramp event classification and prediction. We additionally outline a design for a novel approach to wind ramp prediction, in which high-resolution wind fields are incorporated to the modelling of wind power.
