Optimizing BiLSTM Network Attack Prediction Based on Improved Gray Wolf Algorithm

Qiu, Shaoming; Wang, Yahui; Lv, Yana; Chen, Fen; Zhao, Jiancheng

doi:10.3390/app13126871

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Furthermore, some researchers have introduced intelligent optimization algorithms to enhance the performance of the proposed models, such as Particle Swarm Optimization (PSO) [37], the Grey Wolf Optimizer (GWO) [38], and the Cuckoo Search (CS) [39]. Experimental results indicate that the use of optimization algorithms can improve the predictive capability of the proposed models.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Tang,

Zhang,

Huang

et al. 2024

Applied Sciences

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In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a Simple Recurrent Unit (SRU), K-Nearest Neighbors (KNN), and Hunter–Prey Optimization (HPO). Firstly, the sliding time window is determined by using the autocorrelation function (ACF), partial correlation function (PACF), and model training. The Pearson correlation coefficient method is used to filter the principal meteorological factors that affect photovoltaic power. Then, the K-Nearest Neighbors (KNN) algorithm is utilized for effective outlier detection and processing to ensure the quality of input data for the prediction model, and the Hunter–Prey Optimization (HPO) algorithm is applied to optimize the parameters of the KNN algorithm. Finally, the efficient Simple Recurrent Unit (SRU) model is used for training and prediction, with the Hunter–Prey Optimization (HPO) algorithm applied to optimize the parameters of the SRU model. Simulation experiments and extensive ablation studies using photovoltaic data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, Australia, validate the effectiveness of the integrated model, the KNN outlier handling, and the HPO algorithm. Compared to the Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Simple Recurrent Unit (SRU) models, this model exhibits an average reduction of 19.63% in Mean Square Error (RMSE), 27.54% in Mean Absolute Error (MAE), and an average increase of 1.96% in coefficient of determination (R2) values.

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Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Tang,

Zhang,

Huang

et al. 2024

Applied Sciences

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show abstract

“…Skip connections are used in other neural networks, such as CNN, to fuse different data features. The neural network output features are fused by skip connections [23].…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Output Modeling of Synchronous Condenser in UHVDC Converter Station Based on Interlaced Superposition CNN-BiLSTM

Wang,

et al. 2023

Int J Comput Intell Syst

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To guarantee stable power system operation, a synchronous condenser (SC) is configured in an ultra-high voltage direct current (UHVDC) converter station to provide dynamic reactive power support to the power system. The research on the reactive power output modelling of a SC in an UHVDC converter station has important theoretical significance and practical value for the reactive power control of a SC in an UHVDC converter station. Focusing on the reactive power regulation system of the SC with strong coupling, multivariable, and nonlinear features, it is difficult for the universal analytic method to build the SC reactive power output model. A novel reactive power output model of the SC in the UHVDC converter station based on interlaced superposition convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed, in which a novel interlaced superposition CNN composed of convolution units with two different structures is built to increase the depth of the network and avoid over-fitting. Particularly the branch channels of convolution units with two different structures are connected by a convolution layer and a skip connection respectively. The interlaced superposition CNN-BiLSTM model is that the combination of the interlaced superposition CNN model and BiLSTM model for improving the model accuracy and generic capability. The Bayesian optimization method is used to optimize its hyperparameters. The application of interlaced Superposition CNN-BiLSTM in SC reactive power output modelling is a new technology. The excitation voltage and excitation current of the SC are used as inputs for the training and testing sampled data, and the reactive power of the SC is used as outputs for the training and testing sampled data. Therefore, the reactive power output model of a SC in an UHVDC converter station based on interlaced superposition CNN-BiLSTM is obtained, and it attains a low root mean square error (RMSE = 0.126750) and a high determination coefficient (R2 = 0.999999).

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Intelligent deformation prediction model for concrete dams based on VMD-ResNetPlus-BiLSTM

Fang,

Liu,

et al. 2024

Proceedings of the 2024 International Academic Conference on Edge Computing, Parallel and Distributed Computing

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Optimizing BiLSTM Network Attack Prediction Based on Improved Gray Wolf Algorithm

Cited by 4 publications

References 21 publications

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Ultra-Short-Term Photovoltaic Power Generation Prediction Based on Hunter–Prey Optimized K-Nearest Neighbors and Simple Recurrent Unit

Reactive Power Output Modeling of Synchronous Condenser in UHVDC Converter Station Based on Interlaced Superposition CNN-BiLSTM

Intelligent deformation prediction model for concrete dams based on VMD-ResNetPlus-BiLSTM

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