Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression

Hui Liu, Xiwei Mi, Yanfei Li, Zhu Duan and Yinan Xu

Renewable Energy, 2019, vol. 143, issue C, 842-854

Abstract: Wind speed forecasting can effectively improve the safety and reliability of wind energy generation system. In this study, a novel hybrid short-term wind speed forecasting model is proposed based on the SSA (Singular Spectrum Analysis) method, CNN (Convolutional Neural Network) method, GRU (Gated Recurrent Unit) method and SVR (Support Vector Regression) method. In the proposed SSA-CNNGRU-SVR model, the SSA is used to decompose the original wind speed series into a number of components as: one trend component and several detail components; the CNNGRU is used to predict the trend component, while the SVR is used to predict the detail components. To investigate the prediction performance of the proposed model, several models are used as the benchmark models, including the ARIMA model, PM model, GRU model, LSTM model, CNNGRU model, hybrid SSA-SVR model and hybrid SSA-CNNGRU model. The experimental results show that: in the proposed model, the CNNGRU can have good prediction performance in the main trend component forecasting, the SVR can have good prediction performance in the detail components forecasting, and the proposed model can obtain good results in wind speed forecasting.

Keywords: Wind speed forecasting; Singular spectrum analysis; Convolutional gated recurrent unit network; Support vector regression; Time series; Deep learning (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (40)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148119306998
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:143:y:2019:i:c:p:842-854

DOI: 10.1016/j.renene.2019.05.039

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().