Abstract
Deep Learning Convolutional Neural Networks have been successfully used in many applications. Its versatility lies in reducing the number of parameters to train while maintaining or improving the feature representation capabilities offered by other architectures. Due to its success, Convolutional Networks have become the architecture of choice for image and video processing applications. The application of Convolutional Networks to wind time series is still limited, being an area with high potential for developing new approaches. This paper explores several deep learning models and applies them to wind time series for multi-step forecasting. The time series used for the experimentation are multidimensional time-stamped multi-variate meteorological data. We use a large dataset of wind data from the National Renewable Laboratory with 126,692 wind sites, requiring the use of High Performance Computing. The experimentation results show how Convolutional Networks are a valid approach for wind time series forecasting.
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Acknowledgements
The authors would like to thank the Barcelona Supercomputing Center (BSC) for the usage of their resources and the United States National Renewable Laboratory (NREL) for the use of its Wind Toolkit (wind datasets). We would also like to thank the anonymous reviewers for providing valuable comments that helped to improve the quality of this paper. Prof. U. Cortés is a member of the Sistema Nacional de Investigadores (level III) (CONACyT-Mexico).
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Manero, J., Béjar, J., Cortés, U. (2022). Wind Prediction Using Deep Learning and High Performance Computing. In: Gitler, I., Barrios Hernández, C.J., Meneses, E. (eds) High Performance Computing. CARLA 2021. Communications in Computer and Information Science, vol 1540. Springer, Cham. https://doi.org/10.1007/978-3-031-04209-6_14
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