An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm

Hui Liu, Xiwei Mi and Yanfei Li

Renewable Energy, 2018, vol. 123, issue C, 694-705

Abstract: The wind speed forecasting is important for the wind power industry to achieve the intelligent management. Three new hybrid methods using the WPD (Wavelet Packet Decomposition), the EMD (Empirical Mode Decomposition) and the ELM (Extreme Learning Machine) are presented for wind speed multi-step predictions. In the proposed architectures, the WPD is chosen to decompose the actual wind speed series into several sub-layers, while the EMD is adopted to further decompose the obtained LF (Low Frequency) sub-layers, the obtained HF (High Frequency) sub-layers and all the obtained sub-layers into a number of IMFs (Intrinsic Mode Functions), respectively. Finally, the ELM is used to complete the wind speed predicting computation for these decomposed wind speed sub-layers. To investigate the performance of the proposed hybrid models in the wind speed multi-step forecasting and find which kind of signal decomposing approach is the most suitable for the ELM based wind speed forecasting, the PM (Persistent Model), the ARIMA model, the SVM model, the ELM model, the WPD-ELM model, the WPD-EMD (LF)-ELM model, the WPD-EMD (HF)-ELM model and the WPD-EMD-ELM model are all included in the forecasting performance comparisons. The results of the two real experiments indicate that among all the involved models, the WPD-EMD (LF)-ELM model has the best predicting performance.

Keywords: Wind speed predictions; Wind speed decomposing performance; Wavelet packet decomposition; Empirical mode decomposition; Extreme learning machine (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (38)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148118302362
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:123:y:2018:i:c:p:694-705

DOI: 10.1016/j.renene.2018.02.092

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 ().