A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind
  Prediction
Fanling Huang Yangdong Deng
The prediction of wind in terms of both wind speed and direction, which has a
crucial impact on many real-world applications like aviation and wind power
generation, is extremely challenging due to the high stochasticity and
complicated correlation in the weather data. Existing methods typically focus
on a sub-set of influential factors and thus lack a systematic treatment of the
problem. In addition, fine-grained forecasting is essential for efficient
industry operations, but has been less attended in the literature. In this
work, we propose a novel data-driven model, Multi-Horizon SpatioTemporal
Network (MHSTN), generally for accurate and efficient fine-grained wind
prediction. MHSTN integrates multiple deep neural networks targeting different
factors in a sequence-to-sequence (Seq2Seq) backbone to effectively extract
features from various data sources and produce multi-horizon predictions for
all sites within a given region. MHSTN is composed of four major modules.
First, a temporal module fuses coarse-grained forecasts derived by Numerical
Weather Prediction (NWP) and historical on-site observation data at stations so
as to leverage both global and local atmospheric information. Second, a spatial
module exploits spatial correlation by modeling the joint representation of all
stations. Third, an ensemble module weighs the above two modules for final
predictions. Furthermore, a covariate selection module automatically choose
influential meteorological variables as initial input. MHSTN is already
integrated into the scheduling platform of one of the busiest international
airports of China. The evaluation results demonstrate that our model
outperforms competitors by a significant margin.