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

Neeraj Bokde,Andrés Feijóo,Daniel Villanueva,Kishore Kulat

doi:10.3390/en12020254

Neeraj Bokde, Andrés Feijóo + Show 2 more

Open Access

https://doi.org/10.3390/en12020254

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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.

Highlights

Wind power is a clean, renewable and green source of energy
This paper presents a wide review of empirical decomposition methods applied to wind speed and wind power prediction
It is being assumed that wind speed and wind power prediction techniques can be classified into three different groups, known as the physical, the statistical and the intelligent approaches

Summary

Introduction

Wind power is a clean, renewable and green source of energy. The world is encouraged to use the wind as a green, clean and free energy source [1,2]. The uncertain nature of wind is one of the main concerns in the process of wind energy generation because the availability of wind is the factor that majorly affects wind energy generation process. For energy managers and electricity operators, in order to reduce the uncertainty of the chaotic nature of the wind, an accurate and more precise prediction of wind speed and power became the utmost important task. The accurate wind prediction can be used to understand the wind energy potential, in wind farm designs, managements of wind farms and power grids, and much more [3].

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