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Título : Multi-horizon Scalable Wind Power Forecast System
Autor : Valenzuela, Camilo
Allende, Héctor
Valle, Carlos
Palabras clave : WIND POWER FORECAST;DISTRIBUTED SYSTEM;RECURRENT NEURAL NETWORK;LONG-SHORT TERM MEMORY;ECHO STATE NETWORK;ARIMA
Fecha de publicación : 2018
Editorial : Springer
Citación : Valenzuela C., Allende H., Valle C. (2018) Multi-horizon Scalable Wind Power Forecast System. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_36
Resumen : Wind power is the Non-Conventional Renewable Energy that has become more relevant in recent years. Given the stochastic behavior of wind speed it is necessary to have efficient prediction models at different horizons. Several kind of models have been used to forecast wind power, but using the same kind of model to forecast at different horizons is not recommendable, therefore a multi-model system needs to be implemented. We propose an scalable wind power forecasting system for multiple horizons using open source software, focusing on the forecast model selection, validated with Chilean wind farms data. Showing that RNN models can make significantly better forecasts than traditional models and can scale easily.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9487
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