Wind speed prediction using multidimensional convolutional neural networks

• URL: http://arxiv.org/abs/2007.12567v1
• Date: Sat, 4 Jul 2020 20:48:41 GMT
• Title: Wind speed prediction using multidimensional convolutional neural networks
• Authors: Kevin Trebing and Siamak Mehrkanoon
• Abstract summary: This paper introduces a model based on convolutional neural networks (CNNs) for wind speed prediction tasks. We show that compared to classical CNN-based models, the proposed model is able to better characterise the wind data.
• Score: 5.228711636020665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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