Deep shared representation learning for weather elements forecasting

This paper introduces a data-driven predictive model based on deep convolutional neural networks (CNN) architecture for wind speed prediction in weather data. The model exploits the spatio-temporal multivariate weather data for learning shared representations and forecasting weather elements for a number of user defined weather stations simultaneously in an end-to-end fashion. The embedded feature learning component of the model as well as coupling the learned features of different input layers have shown to have a significant impact on the prediction task. An experimental setup has been considered based on a high temporal resolution dataset collected from the National Climatic Data Center (NCDC) at five stations located in Denmark. The experiment concerns wind speed prediction at three weather stations located in Denmark for 6 and 12 hours ahead.