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Título: | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
Autor: | Flieller Alfonso, Guillermo Francisco Solari, Alfredo Bruno Cotelo, Rafael Chaer, Ruben |
Tipo: | Ponencia |
Palabras clave: | Training, Adaptation models, Uncertainty, Wind speed, Wind power generation, Predictive models, Wind farms, Renewable energy systems, Forecasting, Wind energy, Neural networks, Wind turbine power curve |
Cobertura geográfica: | Uruguay |
Fecha de publicación: | 2023 |
Resumen: | In systems with a high penetration of wind power generation, the precision of the forecasts is a critical input for the electricity dispatch planning. In this paper, we present the methodology that has been used to implement a complete update of the wind power forecast model in Uruguay. The new model increases the precision of the forecasts both in low and high power scenarios. It allows to perform a more efficient short-term electricity dispatch, improving the resource valuation, the inter-systems energy exchanges and the prevision of the wholesale electricity market spot price. According to the simulations performed, the new model increase the precision of wind power forecasts between 7% and 32%. The model is on its production phase and their results can be accessed through pronos.adme.com.uy/svg and latorrex.adme.com.uy/vates. |
EN: | 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5. |
Citación: | Flieller Alfonso, G., Solari, A., Bruno Cotelo, R. y otros. A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system [en línea]. EN: 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5. DOI: 10.1109/ICECET58911.2023.10389491. |
Licencia: | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | ||
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FSBC23.pdf | Versión enviada | 1,63 MB | Adobe PDF | Visualizar/Abrir |
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