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

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 hi...

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Vydané v:	Energy (Oxford) Ročník 244; s. 122585
Hlavní autori:	Krishna Rayi, Vijaya, Mishra, S.P., Naik, Jyotirmayee, Dash, P.K.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 01.04.2022 Elsevier BV
Predmet:	Adaptive variational mode decomposition Algorithms Back propagation Back propagation networks California Deep learning Deep learning kernel ELM Autoencoder energy Exact solutions Forecasting Heuristic methods Hydrologic cycle Kernels Machine learning Mathematical models Multistep prediction Neural networks Noise prediction Optimization Outliers (statistics) Parameters prediction Sine cosine water cycle algorithm Spain time series analysis Trigonometric functions wind Wind farms Wind power Wind power prediction Wyoming Wyoming California Spain Multistep prediction Deep learning kernel ELM Autoencoder Adaptive variational mode decomposition Wind power prediction Sine cosine water cycle algorithm
ISSN:	0360-5442, 1873-6785
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Shrnutí:	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. [Display omitted] •A new Deep learning mixed kernel ELM-AE is presented for wind power prediction.•Adaptive VMD is used to select suitable IMFs as inputs to Deep MKELM Autoencoder.•MKELM-AE parameters are optimized using a novel Sine Cosine Water Cycle Algorithm.•Multi step forecasting of wind power is achieved using the Robust MKELM-AE.•High prediction accuracy is achieved using data from three wind farms.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0360-5442 1873-6785
DOI:	10.1016/j.energy.2021.122585