Título:
A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system
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Autor:
Flieller Alfonso, Guillermo Francisco
Solari, Alfredo
Bruno Cotelo, Rafael
Chaer, Ruben
Solari, Alfredo
Bruno Cotelo, Rafael
Chaer, Ruben
Tutor:
Tipo de documento:
Ponencia
Editor:
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
Adaptation models
Uncertainty
Wind speed
Wind power generation
Predictive models
Wind farms
Renewable energy systems
Forecasting
Wind energy
Neural networks
Wind turbine power curve
Descriptores:
Año de publicación:
2023
Contenido:
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.
Descripción:
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EN:
2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5.
Financiadores:
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.
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Cobertura geográfica:
Uruguay
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| FSBC23.pdf | Versión enviada | 1.59 MB | Adobe PDF |