Prediction of Wind Speed with optimized EMD based RVFLN

Jalli, Ravi Kumar; Mishra, Smita; Naik, Jyotirmayee; Sharma, Priyansha; Senapati, Rudranarayan

doi:10.1109/hydcon48903.2020.9242739

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…Data from wind farms of the states of Wyoming and California (USA) and Spain are used as case of study. In [145] a hybrid short-term wind speed forecasting approach is proposed combining the empirical mode decomposition technique with RVFL. The empirical mode decomposition divides the wind speed input data into a fixed number of intrinsic mode function.…”

Section: Rvfl For Wind Speed Prediction Problemsmentioning

confidence: 99%

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Ser¹,

Casillas-Pérez²,

Cornejo-Bueno³

et al. 2021

Preprint

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Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining predic-

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Section: Rvfl For Wind Speed Prediction Problemsmentioning

confidence: 99%

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Ser¹,

Casillas-Pérez²,

Cornejo-Bueno³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…In [16], the EMD was used to obtain a diagnosis of potential failures in industrial machines. Two methods were proposed for wind speed prediction in power generation systems, one based on the EMD and random vector functional link network [17] and the other, combining the random forest model with a variant of the EMD [18]. An innovative predictor of daily solar radiation has been developed by integrating EMD and a back-propagation neural network to improve the efficiency of intelligent photovoltaic systems [19].…”

Section: Introductionmentioning

confidence: 99%

Empirical Mode Decomposition: Theory and Applications in Underwater Acoustics

Filho

Santos

Sinezio

et al. 2022

JCIS

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Empirical mode decomposition (EMD) is a signal processing method that produces a data-driven multiresolution representation in time-domain suited to characterize time-varying and nonlinear phenomena. In EMD, intrinsic mode functions (IMF) are sequentially estimated from the signal of interest to represent different intrinsic oscillation modes and produce an orthogonal representation of the original information. Different algorithms have been proposed for IMF estimation to deal with limitations such as mode-mixing and noise sensitivity. EMD is usually associated with the Hilbert transform to obtain a frequency-domain representation. In this case, the method is referred to as the Hilbert-Huang transform (HHT). This paper presents a theoretical review of the fundamental aspects of both EMD and HHT, such as the IMF estimation procedure. Variations of the original EMD algorithm are also presented. Both simulated and experimental underwater acoustic signals are used to illustrate the efficiency of EMD/HHT in revealing relevant characteristics from time-varying and nonlinear phenomena.

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