A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine

Meng, Anbo; Zhu, Zibin; Deng, Weisi; Ou, Zuhong; Lin, Shu; Wang, Chenen; Xu, Xuancong; Wang, Xiaolin; Yin, Hao; Luo, Jianqiang

doi:10.1016/j.energy.2022.124957

Cited by 32 publications

(4 citation statements)

References 67 publications

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“…The specific allocation methods are listed in table 6. The same training and test sequence samples were used to experiment with the PSO-least squares support vector machine (PSO-LSSVM) [47], long short-term memory (LSTM) [48], DELM [15], SSAE [49], recurrent neural network (RNN) [50], and dung beetle optimizer optimizes DELM (DBO-DELM) [51], and compared with the proposed model.…”

Section: The Proposed Model Performance Validation Methodmentioning

confidence: 99%

“…Although most studies of ML algorithms based on MSS have skyrocketing prediction effects on tool wear values, the stability of model predictions is ignored. The stability of model predictions refers to the ability to maintain ideal prediction performance in the face of new data [15]. Therefore, considering the precision and smoothness of tool wear prediction under complex machining environments, different processing parameters, and different tools remains a challenge for some learning models [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Zhou,

Xiao,

et al. 2024

Meas. Sci. Technol.

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Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELM) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, PSO-LSSVM, LSTM, SSAE, RNN, and DBO-DELM.

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Section: The Proposed Model Performance Validation Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Zhou,

Xiao,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Zhang et al [122] applied the improved sine and cosine algorithm (ISCA) to optimize the parameters of the BiLSTM model, achieving superior optimization results compared to the standard SCA. In [123], to ensure the accuracy and stability of WPP, the key parameters of the DELM model underwent optimization through multi-objective crisscross optimization (MOCSO). In [105], an improvement to the PSO was employed to optimize the optimal number of hidden neurons and the optimal learning rate for the LSTM model, significantly enhancing the short-term accuracy of WPP.…”

Section: Parameter Optimizationmentioning

confidence: 99%

“…ISCA [122], MOCSO [123], PSO [105], IChOA [124], SSO [125], HBO [130], MOECO [126], JADE [127], MOSMA [128], CSSOA [129]…”

Section: Parameter Optimizationmentioning

confidence: 99%

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

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Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

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A survey of long short term memory and its associated models in sustainable wind energy predictive analytics

Garg

Krishnamurthi

2023

Artif Intell Rev

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A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine

Cited by 32 publications

References 67 publications

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

A survey of long short term memory and its associated models in sustainable wind energy predictive analytics

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