Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast

Sinvaldo Rodrigues Moreno, Viviana Cocco Mariani and Leandro dos Santos Coelho

Renewable Energy, 2021, vol. 164, issue C, 1508-1526

Abstract: Wind speed forecasting is a challenging task, especially medium-term forecasting on 10 min sampling interval, due to the intermittent nature of wind. Several numerical methods were presented to improving wind speed forecasting accuracy, being the decomposition strategy reported as the key step to improve forecasting results, where the raw wind speed data is first decomposed into several patterns and then recomposed in a new time series. This can effectively reduce the uncertainty in the wind speed forecasting, leading us to believe that the precision of the forecast has a strong correlation with the preprocessing strategy, rather than with the forecasting technique. Based on this understanding, this paper combines two decomposition techniques, amplitude and frequency modulation-demodulation signal theory. The proposed decomposition approach is further combined with the parametric forecasting model named AutoRegressive Integrated Moving Average (ARIMA), resulting in a new ensemble learning method called Variational Mode Decomposition-Singular Spectral Analysis-AutoRegressive Integrated Moving Average (VMD-SSA-ARIMA). All forecasting results obtained by this ensemble are compared with those from the ARIMA. The ensemble results demonstrated stabilization of the forecasting errors. This indicates the ability of the proposed hybrid approach to decompose wind speed time series into uncorrelated components, reducing the errors from one up to a 12-steps-ahead forecasting horizon.

Keywords: Wind speed; Decomposition based methods; Variational mode decomposition; Spectrum singular analysis; Time series (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:164:y:2021:i:c:p:1508-1526

DOI: 10.1016/j.renene.2020.10.126

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