Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting

Vijaya Krishna Rayi, S.P. Mishra, Jyotirmayee Naik and P.K. Dash

Energy, 2022, vol. 244, issue PA

Abstract: In this paper, an efficient new hybrid time series forecasting model combining variational mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder (AE) has been presented for precise prediction of wind power. It is well known that KELM-AE avoids manual tuning of weights of hidden layer nodes, and exhibits superb model generalization property, reduces execution time, and produces exact solution of output weights by generalized least squares resulting in compact storage space, etc. This is in contrast to other deep learning neural network models with nonconvex optimization problems, time consuming training process and limitations of backpropagation learning algorithms. Further the VMD parameters (α, K) are optimized to yield suitable number of intrinsic modes (IMFs) using a newly presented meta-heuristic population based Sine Cosine integrated water cycle algorithm (SCWCA) algorithm. Further the mixed kernel parameters and their associated weights are optimized using the same SCWCA to improve the performance of the Deep MKELM predictor against any outliers or noise in the data. Wind power data of three wind farms located in Sotavento Spain, Wyoming, and California, USA are applied for short-term and multi-step prediction of wind power with significant accuracy.

Keywords: Wind power prediction; Adaptive variational mode decomposition; Sine cosine water cycle algorithm; Deep learning kernel ELM Autoencoder; Multistep prediction (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221028346
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:energy:v:244:y:2022:i:pa:s0360544221028346

DOI: 10.1016/j.energy.2021.122585

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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