Deep shared representation learning for weather elements forecasting

Siamak Mehrkanoon*

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

Abstract

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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2491
Publication statusPublished - 2019
Event31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019 - Brussels, Belgium
Duration: 6 Nov 20198 Nov 2019

Bibliographical note

Funding Information:
Acknowledgments. This work was partially supported by the Postdoctoral Fellowship of the Research Foundation-Flanders (FWO: 12Z1318N). Siamak Mehrkanoon is an assistant professor at the Department of Data Science and Knowledge Engineering, Maastricht University, The Netherlands.

Publisher Copyright:
© 2019 for this paper by its authors.

Funding

Acknowledgments. This work was partially supported by the Postdoctoral Fellowship of the Research Foundation-Flanders (FWO: 12Z1318N). Siamak Mehrkanoon is an assistant professor at the Department of Data Science and Knowledge Engineering, Maastricht University, The Netherlands.

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