Localized Convolutional Neural Networks for Geospatial Wind Forecasting

Arnas Uselis, Mantas Lukoševičius and Lukas Stasytis
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Arnas Uselis: Faculty of Informatics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
Mantas Lukoševičius: Faculty of Informatics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
Lukas Stasytis: Faculty of Informatics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania

Energies, 2020, vol. 13, issue 13, 1-21

Abstract: Convolutional Neural Networks (CNN) possess many positive qualities when it comes to spatial raster data. Translation invariance enables CNNs to detect features regardless of their position in the scene. However, in some domains, like geospatial, not all locations are exactly equal. In this work, we propose localized convolutional neural networks that enable convolutional architectures to learn local features in addition to the global ones. We investigate their instantiations in the form of learnable inputs, local weights, and a more general form. They can be added to any convolutional layers, easily end-to-end trained, introduce minimal additional complexity, and let CNNs retain most of their benefits to the extent that they are needed. In this work we address spatio-temporal prediction: test the effectiveness of our methods on a synthetic benchmark dataset and tackle three real-world wind prediction datasets. For one of them, we propose a method to spatially order the unordered data. We compare the recent state-of-the-art spatio-temporal prediction models on the same data. Models that use convolutional layers can be and are extended with our localizations. In all these cases our extensions improve the results, and thus often the state-of-the-art. We share all the code at a public repository.

Keywords: convolutional neural networks; recurrent neural networks; deep learning; machine learning; spatial-temporal wind forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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