A Hybrid Model Based on Multi-Stage Principal Component Extraction, GRU Network and KELM for Multi-Step Short-Term Wind Speed Forecasting

Zou, Feng; Fu, Wenlong; Fang, Ping; Xiong, Dongzhen; Wang, Renming

doi:10.1109/access.2020.3043812

Cited by 21 publications

(5 citation statements)

References 45 publications

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“…EMD and EEMD algorithms) which optimize the signal characterization of the original signal by adding different levels of white noise to optimize its signal representation and overcome the phenomenon of mode aliasing. While EEMD still has the disadvantages of significant noise and low computational efficiency, CEEMDAN is an improvement for the above disadvantages of EEMD, which reconstructs the signal infinitely close to the original signal by adding a finite amount of adaptive white noise conforming to the standard normal distribution in each iteration [26]. The method of decomposition uses a small number of experiments to reconstruct the signal sequence, and it can effectively remove the incompleteness, sizeable computational effort, and computational time of EEMD.…”

Section: Ceemdan Data Processing Frameworkmentioning

confidence: 99%

“…For example, Guo et al combined CEEMDAN with long short-term memory (LSTM) to reduce the difficulty of predicting chaotic time series and improve prediction accuracy [25]. Zou et al combined CEEMDAN, singular spectrum analysis (SSA), and phase space reconstruction (PSR) to achieve higher multilevel prediction accuracy [26]. Wang et al utilized the effectiveness of CEEMDAN denoising with a GRU model to obtain the long-term dependence characteristics of the data, and utilized the attention mechanism to adjust the information weights to improve the accuracy and robustness of the prediction [27].…”

Section: Introductionmentioning

confidence: 99%

“…Zou et al. combined CEEMDAN, singular spectrum analysis (SSA), and phase space reconstruction (PSR) to achieve higher multilevel prediction accuracy [26]. Wang et al.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid load prediction model of 5G base station based on time series decomposition and GRU network with parameter optimization

Hua,

Sun,

2024

IET Generation Trans & Dist

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To ensure the safe and stable operation of 5G base stations, it is essential to accurately predict their power load. However, current short‐term prediction methods are rarely applied rationally in pertinent circumstances to the features of base station power load over time. For high accuracy and generalization capabilities, this work proposes a hybrid approach that combines gated recurrent unit (GRU) with particle swarm optimization (PSO) and completes ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The complex load is decomposed into multiple components to improve the fit of the neural network. To deal with the nonlinearity that constrains the modal aliasing and data noise, CEEMDAN is used to reconstruct the trend and noise sequences. The GRU network is utilized to improve the neural network fitness and obtain long‐term features. In addition, PSO is used to optimize the number of neurons and the learning rate of the GRU. Compared with existing neural network GRUs, the RMSE is reduced by 16.6, the mean absolute error is reduced by 11.97, and the coefficient of R2 is improved by 0.13, indicating that the model has a better fitting effect. The comparisons prove that the proposed model has better accuracy than the existing methods.

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Section: Ceemdan Data Processing Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid load prediction model of 5G base station based on time series decomposition and GRU network with parameter optimization

Hua,

Sun,

2024

IET Generation Trans & Dist

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“…The KELM ( Tian et al, 2019 ; Zhang et al, 2020 ; Zou et al, 2020 ; Chen H. et al, 2021 ; Wang and Wang, 2021 ) is an improved technology based on the Extreme Learning Machine (ELM) combined with a kernel function, which improves the predictive performance of the model while retaining the benefits of the ELM and is a single hidden feedforward neural network with a three-layer independent layer structure, including the input layer, the output layer, and the implicit layer. For a training set with N samples: S =( x j , t j ) ∈ R n × R m , its target learning function model F ( x ) can be expressed in Eq.…”

Section: The Proposed Bcowoa-kelm Modelmentioning

confidence: 99%

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Zhao²,

Liu³

et al. 2022

Front. Neuroinform.

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Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.

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“…Due to the characteristics [ 34 , 35 ] of ELM's random input weight and biases, the stability of ELM prediction is poor. However, kernel extreme learning machine (KELM) overcomes the shortcoming of poor stability of ELM prediction and improves the prediction accuracy of the algorithm [ 36 , 37 ]. KELM have been widely confirmed and applied to various fields of forecasting.…”

Section: Introductionmentioning

confidence: 99%

A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases

Yang

Liu

2023

Computers in Biology and Medicine

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A Hybrid Model Based on Multi-Stage Principal Component Extraction, GRU Network and KELM for Multi-Step Short-Term Wind Speed Forecasting

Cited by 21 publications

References 45 publications

Hybrid load prediction model of 5G base station based on time series decomposition and GRU network with parameter optimization

Hybrid load prediction model of 5G base station based on time series decomposition and GRU network with parameter optimization

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases

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