Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
Artículo de revista
2021-05-18
Corporación Universidad de la Costa
https://doi.org/https://doi.org/10.18280/mmep.080313
2369-0739
The limited availability of local climatological stations and the limitations to predict the wind speed (WS) accurately are significant barriers to the expansion of wind energy (WE) projects worldwide. A methodology to forecast accurately the WS at the local scale can be used to overcome these barriers. This study proposes a methodology to forecast the WS with high-resolution and long-term horizons, which combines a Fourier model and a nonlinear autoregressive network (NAR). Given the nonlinearities of the WS variations, a NAR model is used to forecast the WS based on the variability identified with the Fourier analysis. The NAR modelled successfully 1.7 years of windspeed with 3 hours of the time interval, what may be considered the longest forecasting horizon with high resolution at the moment.
- Artículos científicos [2641]
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Modeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model.pdf
Título: Modeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model.pdf
Tamaño: 1.389Mb
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Título: Modeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model.pdf
Tamaño: 1.389Mb



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