Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis

Zhu, Yingqi

doi:10.1007/s11356-022-22957-2

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…Classical feature extraction methods encompass phase space reconstruction (PSR) [16], granger causality testing (GCT) [64], autocorrelation function (ACF), partial ACF (ACF), recursive feature elimination (RFE) [65], mutual information (MI), grey relation analysis (GRA) [66], and principal component analysis (PCA) [67]. Unlike other selection-based feature extraction methods, PCA is a dimensionality reduction-based feature selection method that generates new features during the feature extraction process.…”

Section: Classical Feature Extraction Methodsmentioning

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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Section: Classical Feature Extraction Methodsmentioning

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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“…Calculating the degree of correlation between various factors and wind power, the method determines the magnitude of each factor's influence on wind power and selects several factors with the highest correlation as input parameters for prediction. This method can effectively improve the accuracy of predictions, investigate the laws and characteristics underlying wind power data, and avoid the errors caused by an excessive amount of balanced data input [35]. The specific analysis steps are as follows:…”

Section: Gray Relation Analysismentioning

confidence: 99%

Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain

et al. 2023

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Accurate wind power data prediction is crucial to increase wind energy usage since wind power data are characterized by uncertainty and randomness, which present significant obstacles to the scheduling of power grids. This paper proposes a hybrid model for wind power prediction based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), bidirectional long short-term memory network (BiLSTM), and Markov chain (MC). First, CEEMDAN is used to decompose the wind power series into a series of subsequences at various frequencies, and then SE is employed to reconstruct the wind power series subsequences to reduce the model’s complexity. Second, the long short-term memory (LSTM) network is optimized, the BiLSTM neural network prediction method is used to predict each reconstruction component, and the results of the different component predictions are superimposed to acquire the total prediction results. Finally, MC is used to correct the model’s total prediction results to increase the accuracy of the predictions. Experimental validation with measured data from wind farms in a region of Xinjiang, and computational results demonstrate that the proposed model can better fit wind power data than other prediction models and has greater prediction accuracy and generalizability for enhancing wind power prediction performance.

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Long, short, and medium terms wind speed prediction model based on LSTM optimized by improved moth flame optimization algorithm

Li,

Wang,

et al. 2024

Environ Sci Pollut Res

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Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis

Cited by 5 publications

References 52 publications

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

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

Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain

Long, short, and medium terms wind speed prediction model based on LSTM optimized by improved moth flame optimization algorithm

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