Abstract
This paper presents a novel feed-forward neural network for wind speed forecasting. The electricity sector accounts for a quarter of the world CO2 emissions. To reduce these emissions, several national, regional and global agreements have been signed, setting ambitious goals to increase the penetration of renewable energy sources (RES). Although achieving those goals is essential for the sector decarbonization and, therefore, to mitigate the global climate crisis, renewable-based generation can depend on highly variable and uncertain resources, such as the wind. Hence, having access to reliable forecasts of those resources availability is essential for the operation of several actors in the power and energy sector, and for the effectiveness of the whole system. This paper contributes to surpass this problem by introducing a new forecasting model based on a feed-forward neural network to forecast wind speed. The proposed model is applied to real data from a wind farm in the south of South America. Results show that the proposed model can achieve lower forecasting errors than the baseline models, which consist of Numerical Weather Predictions.
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Acknowledgements
Tiago Pinto received funding from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760/2020. Hugo Morais was supported by national funds through FCT, Fundação para a Ciência e a Tecnologia, under project UIDB/50021/2020.
The authors would like to thank Cepel for the granted master’s scholarship and for the support for the development of this research, Eletrobras for providing the data and Cepel’s researchers Vanessa Guedes and Ricardo Dutra for their assistance with data analysis.
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Machado, E.P., Morais, H., Pinto, T. (2022). Wind Speed Forecasting Using Feed-Forward Artificial Neural Network. In: Matsui, K., Omatu, S., Yigitcanlar, T., González, S.R. (eds) Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-86261-9_16
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