Abstract
In recent years, electricity generated from renewable energy sources has become a significant contributor to power supply systems over the world. Wind is one of the most important renewable energy sources, thus accurate wind energy prediction is a vital component of the management and operation of electric grids. This paper proposes a novel method for wind energy forecasting, which relies on a novel variant of the scaled-dot product attention mechanism, for exploring relations between the generated energy and a set of multiple-location weather forecasts/measurements. The conducted experimental evaluation on a dataset consisting of the hourly generated wind energy in Greece along with hourly weather forecasts for 18 different locations, demonstrated that the proposed approach outperforms competitive methods.
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Acknowledgements
This work is co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code: T2EDK-03048).
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Symeonidis, C., Nikolaidis, N. (2023). Wind Energy Prediction Guided by Multiple-Location Weather Forecasts. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_37
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DOI: https://doi.org/10.1007/978-3-031-34204-2_37
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