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Deep Learning for Big Data Time Series Forecasting Applied to Solar Power

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 771)

Abstract

Accurate solar energy prediction is required for the integration of solar power into the electricity grid, to ensure reliable electricity supply, while reducing pollution. In this paper we propose a new approach based on deep learning for the task of solar photovoltaic power forecasting for the next day. We firstly evaluate the performance of the proposed algorithm using Australian solar photovoltaic data for two years. Next, we compare its performance with two other advanced methods for forecasting recently published in the literature. In particular, a forecasting algorithm based on similarity of sequences of patterns and a neural network as a reference method for solar power forecasting. Finally, the suitability of all methods to deal with big data time series is analyzed by means of a scalability study, showing the deep learning promising results for accurate solar power forecasting.

Keywords

Deep learning Big data Solar power Time series forecasting 

Notes

Acknowledgments

The authors would like to thank the Spanish Ministry of Economy and Competitiveness and Junta de Andalucía for the support under projects TIN2014-55894-C2-R and P12-TIC-1728, respectively.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Computer ScienceUniversidad Pablo de OlavideSevilleSpain
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia

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