Empirical mode decomposition and chaos based prediction model for wind speed oscillations

Drisya, G. V.; Kumar, K. Satheesh

doi:10.1109/raics.2015.7488433

Cited by 1 publication

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Refs. [29][30][31] proposed hybrid models based on chaotic theory principles to minimize the effects of chaotic nature of measured data on the prediction accuracy.…”

Section: Chaotic Theory Treatmentsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…Refs. [29][30][31] proposed hybrid models based on chaotic theory principles to minimize the effects of chaotic nature of measured data on the prediction accuracy.…”

Section: Chaotic Theory Treatmentsmentioning

confidence: 99%

“…However, in [29,31], the Local First Order (LFO) [130] and Echo State network (ESN) [131] methods were used, respectively. The motivation behind using these methods was to handle the chaotic wind power IMFs generated with EMD.…”

Section: Chaotic Theory Treatmentsmentioning

confidence: 99%