Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm

Suo, Leiming; Tian, Peng; Song, Shihao; Zhang, Chu; Wang, Yuhan; Fu, Yongyan; Nazir, Muhammad Shahzad

doi:10.1016/j.energy.2023.127526

Cited by 55 publications

(7 citation statements)

References 36 publications

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“…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. Suo et al [124] utilized the improved chimp optimization algorithm (IChOA) to optimize parameters for the BiGRU, proving its effectiveness in improving the predictive performance of BiGRU. Through swarm spider optimization (SSO), Wei et al [125] optimized the number of hidden layer nodes for DBN, significantly improving the prediction performance of DBN for wind prediction.…”

Section: Parameter Optimizationmentioning

confidence: 99%

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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: 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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“…Recognizing that the individual new intrinsic mode function (NIMF) components after secondary decomposition contain different feature information, BiGRU is introduced in this study to predict the individual NIMF components. GRU is a structurally simpler version of the LSTM that overcomes the long dependency problem of recurrent neural networks (Cho et al, 2014;Suo et al, 2023). GRU mainly consists of an update gate, which determines for the present time step how much past information has been retained, and a reset gate, which determines how much historical information has been forgotten.…”

Section: Bidirectional Gated Recurrent Unit (Bigru)mentioning

confidence: 99%

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Wu,

Wang,

Dong

et al. 2023

Water Resources Research

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Accurate runoff forecasting plays a vital role in issuing timely flood warnings. Whereas, previous research has primarily focused on historical runoff and precipitation variability while disregarding other factors’ influence. Additionally, the prediction process of most machine learning models is opaque, resulting in low interpretability of model predictions. Hence, this study develops an ensemble deep learning model to forecast runoff from three hydrological stations. Initially, time‐varying filtered based empirical mode decomposition (TVFEMD) is employed to decompose the runoff series into several internal mode functions (IMFs). Subsequently, the complexity of each IMF component is evaluated by the multi‐scale permutation entropy (MPE), and the IMFs are classified into high‐ and low‐frequency portions based on entropy values. Considering the high‐frequency portions still exhibit great volatility, robust local mean decomposition (RLMD) is adopted to perform secondary decomposition of the high‐frequency portions. Then, the meteorological variables processed by the Relief algorithm and variance inflation factor (VIF) features are employed as inputs, the individual subsequences of secondary and preliminary decomposition as outputs to the bidirectional gated recurrent unit (BiGRU) and extreme learning machine (ELM) models. Random forests (RF) are introduced to nonlinear ensemble the individual predicted sub‐models to obtain the final prediction results. The proposed model outperforms other models in various evaluation metrics. Meanwhile, due to the opaque nature of machine learning models, shapley is employed to assess the contribution of each selected meteorological variable to the long‐term trend of runoff. The proposed model could serve as an essential reference for precise flood prediction and timely warning.

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“…Deep learning models are better at predicting network security situations than traditional machine learning models [26], [27], [28]. One reason for this is that these models can infer intricate patterns from data, which traditional models cannot [29], [30], [31].…”

Section: B Related Workmentioning

confidence: 99%

“…(3) Cross operation In the crossover operation, the algorithm transforms the mutation vector 𝑈 𝑖 𝑡 = (𝑢 𝑖,1 𝑡 , 𝑢 𝑖,2 𝑡 , ⋯ , 𝑢 𝑖,𝐷 𝑡 ) into a new vector 𝑉 𝑖 𝑡 = (𝑣 𝑖,1 𝑡 , 𝑣 𝑖,2 𝑡 , ⋯ , 𝑣 𝑖,𝐷 𝑡 ) according to (28).…”

Section: Differential Evolution Strategymentioning

confidence: 99%

Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

Zhang,

Fu,

2023

IEEE Access

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The widespread adoption of Internet-of-Things (IoT) devices has resulted in a comprehensive transformation of human life. However, the network security challenges posed by the IoT devices have become increasingly severe, necessitating the implementation of effective security mechanisms. Network security situational awareness enables an effective network state prediction for better formulation of network security defense strategies. Existing network security situational prediction methods are typically constrained by situational sequence data, especially those sequences with a high degree of non-stationarity, leading to unstable predictions and low performance. Moreover, in real-world application scenarios, the network security situational sequences are often highly non-stationary. To address these challenges, we introduce a novel hybrid prediction model named Variational Mode Decomposition (VMD) -Dynamic Whale Optimization Algorithm (DWOA) -Bidirectional Gated Recurrent Unit (BiGRU) -Attention Mechanism (ATTN). The proposed model integrates VMD, BiGRU, ATTN, and DWOA. Initially, network security situational awareness sequences are processed using VMD to decompose them into a series of subsequences, thus reducing the non-stationarity of the original sequences. Subsequently, an enhanced DWOA optimization algorithm is introduced for tuning the hyperparameters of the BiGRU-ATTN network. Ultimately, BiGRU-ATTN is employed to predict each of these subsequences, which are then aggregated to yield the final network security situational prediction value. When compared with several existing methods on public network security datasets, the proposed VMD-DWOA-BiGRU-ATTN method demonstrated an improvement in the R^2 values ranging from 6.34% to 52.61%. These results substantiate that the model significantly enhances predictive performance.

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Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm

Cited by 55 publications

References 36 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

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

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