A novel ensemble model of different mother wavelets for wind speed multi-step forecasting

Hui Liu, Zhu Duan, Yanfei Li and Haibo Lu

Applied Energy, 2018, vol. 228, issue C, 1783-1800

Abstract: Accurate wind speed forecasting is essential for smart wind power conversion and integration. In the study, a novel ensemble model, using four novel hybrid models as base predictors to obtain high prediction accuracy, is proposed for the multi-step wind speed forecasting. The hybrid base predictors consist of the Wavelet Packet Decomposition (WPD), the Multi-Objective Grey Wolf Optimizer (MOGWO), the Adaptive Boosting.MRT (AdaBoost.MRT) and the Outlier-Robust Extreme Learning Machine (ORELM). The proposed ensemble model is named as the MOGWO-WPD -AdaBoost.MRT-ORELM model. The accuracy and diversity of the base predictors have significant positive influences on the performance of the proposed ensemble model. To guarantee the diversity of the base predictors, one of the most important hyper-parameters in the WPD computation (i.e., the mother wavelet) for every base predictor is investigated. In addition, the MOGWO is used to assemble the base predictors. By combining various models with different hyper-parameters, the ensemble structure can be used to improve the forecasting performance of the hybrid model with single hyper-parameter. To investigate the performance of the proposed forecasting architecture, four sets of experiments were conducted in the study. The results show that: (a) the proposed ensemble model has good convergence and forecasting performance; (b) the forecasting accuracy of the base predictor increases as the vanishing moment increases; and (c) the proposed ensemble model outperforms other benchmark models significantly.

Keywords: Wind speed forecasting; Wavelet packet decomposition; Mother wavelet; Vanishing moment; AdaBoost.MRT; Outlier-robust 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 (18)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261918310791
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:appene:v:228:y:2018:i:c:p:1783-1800

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2018.07.050

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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