Wind speed prediction using multidimensional convolutional neural networks

Kevin Trebing, Siamak Mehrkanoon*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

5 Citations (Web of Science)


Accurate wind speed forecasting is of great importance for many economic, business and management sectors. This paper introduces a new model based on convolutional neural networks (CNNs) for wind speed prediction tasks. In particular, we show that compared to classical CNN-based models, the proposed model is able to better characterise the spatio-temporal evolution of the wind data by learning the underlying complex input-output relationships from multiple dimensions (views) of the input data. The proposed model exploits the spatio-temporal multivariate multidimensional historical weather data for learning new representations used for wind forecasting. We conduct experiments on two real-life weather datasets. The datasets are measurements from cities in Denmark and in the Netherlands. The proposed model is compared with traditional 2- and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a 2D-CNN model equipped with upscaling and depthwise separable convolutions.

Original languageEnglish
Title of host publication2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Number of pages8
ISBN (Print)9781728125473
Publication statusPublished - 2020
Event2020 IEEE Symposium Series on Computational Intelligence - Online, Canberra, Australia
Duration: 1 Dec 20204 Dec 2020


Conference2020 IEEE Symposium Series on Computational Intelligence
Abbreviated titleIEEE SSCI 2020
Internet address


  • Deep learning
  • Wind speed prediction
  • Convolutional neural networks
  • Feature learning
  • Short-term forecasting

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