Open AccessModeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model
Author(s) -
Juan Gabriel Rueda-Bayona,
Juan José Cabello Eras,
Alexis Sagastume Gutiérrez
Publication year - 2021
Publication title -
mathematical modelling and engineering problems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.26
H-Index - 11
eISSN - 2369-0747
pISSN - 2369-0739
DOI - 10.18280/mmep.080313
Subject(s) - autoregressive model , moment (physics) , interval (graph theory) , term (time) , time horizon , fourier transform , horizon , wind speed , nonlinear system , computer science , fourier series , artificial neural network , fourier analysis , scale (ratio) , meteorology , control theory (sociology) , econometrics , mathematics , mathematical optimization , physics , artificial intelligence , control (management) , mathematical analysis , geometry , classical mechanics , combinatorics , quantum mechanics
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 wind-speed with 3 hours of the time interval, what may be considered the longest forecasting horizon with high resolution at the moment.
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