Multi-modal Representation Learning for Cross-modal Prediction of
  Continuous Weather Patterns from Discrete Low-Dimensional Data
Alif Bin Abdul Qayyum Xihaier Luo Nathan M. Urban Xiaoning Qian Byung-Jun Yoon
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.