SPECIAL ISSUE PAPER
Big data solar power forecasting based on deep learning and multiple data sources
José F. Torres,
Alicia Troncoso,
Irena Koprinska,
Zheng Wang,
Francisco Martínez-Álvarez,
José F. Torres
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Search for more papers by this authorCorresponding Author
Alicia Troncoso
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Correspondence
Alicia Troncoso, Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain.
Email: [email protected]
Search for more papers by this authorIrena Koprinska
School of Computer Science, University of Sydney, Sydney, Australia
Search for more papers by this authorZheng Wang
School of Computer Science, University of Sydney, Sydney, Australia
Search for more papers by this authorFrancisco Martínez-Álvarez
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Search for more papers by this authorJosé F. Torres,
Alicia Troncoso,
Irena Koprinska,
Zheng Wang,
Francisco Martínez-Álvarez,
José F. Torres
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Search for more papers by this authorCorresponding Author
Alicia Troncoso
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Correspondence
Alicia Troncoso, Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain.
Email: [email protected]
Search for more papers by this authorIrena Koprinska
School of Computer Science, University of Sydney, Sydney, Australia
Search for more papers by this authorZheng Wang
School of Computer Science, University of Sydney, Sydney, Australia
Search for more papers by this authorFrancisco Martínez-Álvarez
Data Science and Big Data Lab, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Search for more papers by this authorAbstract
In this paper, we consider the task of predicting the electricity power generated by photovoltaic solar systems for the next day at half-hourly intervals. We introduce DL, a deep learning approach based on feed-forward neural networks for big data time series, which decomposes the forecasting problem into several sub-problems. We conduct a comprehensive evaluation using 2 years of Australian solar data, evaluating accuracy and training time, and comparing the performance of DL with two other advanced methods based on neural networks and pattern sequence similarity. We investigate the use of multiple data sources (solar power and weather data for the previous days, and weather forecast for the next day) and also study the effect of different historical window sizes. The results show that DL produces competitive accuracy results and scales well, and is thus a highly suitable method for big data environments.
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