A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

Neeraj Bokde, Andrés Feijóo, Daniel Villanueva and Kishore Kulat
Additional contact information
Neeraj Bokde: Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India
Andrés Feijóo: Departamento de Enxeñería Eléctrica-Universidade de Vigo, Campus de Lagoas-Marcosende, 36310 Vigo, Spain
Daniel Villanueva: Departamento de Enxeñería Eléctrica-Universidade de Vigo, Campus de Lagoas-Marcosende, 36310 Vigo, Spain
Kishore Kulat: Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India

Energies, 2019, vol. 12, issue 2, 1-42

Abstract: Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

Keywords: wind speed; wind power; EMD; decomposition; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/2/254/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/2/254/ (text/html)

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:gam:jeners:v:12:y:2019:i:2:p:254-:d:197906

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().