World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Wind energy has significant potential to not only reduce greenhouse emission, but also meet the ever increasing demand for energy. To enable the effective utilization of wind energy, addressing the following three challenges in wind data analysis is crucial. Firstly, improving data resolution in various climate conditions to ensure an ample supply of information for assessing potential energy resources. Secondly, implementing dimensionality reduction techniques for data collected from sensors/simulations to efficiently manage and store large datasets. Thirdly, extrapolating wind data from one spatial specification to another, particularly in cases where data acquisition may be impractical or costly. We propose a deep learning based approach to achieve multi-modal continuous resolution wind data prediction from discontinuous wind data, along with data dimensionality reduction.

翻译：全球正在寻求不污染环境的清洁可再生能源，以减少导致全球变暖的温室气体排放。风能不仅具有减少温室气体排放的巨大潜力，还能满足日益增长的能源需求。为有效利用风能，解决风能数据分析中的以下三项挑战至关重要：首先，提高不同气候条件下的数据分辨率，以确保充足的信息供给用于评估潜在能源资源；其次，对传感器/仿真采集的数据应用降维技术，以高效管理和存储大规模数据集；第三，将风数据从一种空间规格外推至另一种，尤其是在数据获取可能不切实际或成本高昂的情况下。我们提出了一种基于深度学习方法，旨在从非连续风数据实现多模态连续分辨率风数据预测，同时完成数据降维。