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.
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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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Torres, J.F., Troncoso, A., Koprinska, I., Wang, Z., Martínez-Álvarez, F. (2019). Deep Learning for Big Data Time Series Forecasting Applied to Solar Power. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_12
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DOI: https://doi.org/10.1007/978-3-319-94120-2_12
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