A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy

Peng, Haitao; Wen, Weisong; Tseng, Ming‐Lang

doi:10.1007/s00500-021-05961-5

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…However, in the iterative process of the algorithm, the linear change in a cannot effectively reflect the convergence process of the parameters ( Ding, Wu & Zhao, 2020 ; Peng et al, 2021 ). Therefore, the following nonlinear convergence method is applied, as in Eq.…”

Section: Methodsmentioning

confidence: 99%

Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm

Yang

2022

PeerJ Computer Science

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Sea cucumber farming is an important part of China’s aquaculture industry, and sea cucumbers have higher requirements for aquaculture water quality. This article proposes a sea cucumber aquaculture water quality prediction model that uses an improved whale optimization algorithm to optimize the gated recurrent unit neural network(IWOA-GRU), which provides a reference for the water quality control in the sea cucumber growth environment. This model first applies variational mode decomposition (VMD) and the wavelet threshold joint denoising method to remove mixed noise in water quality time series. Then, by optimizing the convergence factor, the convergence speed and global optimization ability of the whale optimization algorithm are strengthened. Finally, the improved whale optimization algorithm is used to construct a GRU prediction model based on optimal network weights and thresholds to predict sea cucumber farming water quality. The model was trained and tested using three water quality indices (dissolved oxygen, temperature and salinity) of sea cucumber culture waters in Shandong Peninsula, China, and compared with prediction models such as support vector regression (SVR), random forest (RF), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory neural network (LSTM). Experimental results show that the prediction accuracy and generalization performance of this model are better than those of the other compared models.

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Section: Methodsmentioning

confidence: 99%

Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm

Yang

2022

PeerJ Computer Science

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“…Peng and others proposed the HWOA-ELM model for load prediction, utilizing HWOA to optimize the parameters of ELM (Extreme Learning Machine). The predictive results show that the ELM model optimized with HWOA has superior predictive performance [51].…”

Section: B Related Workmentioning

confidence: 96%

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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“…To improve the accuracy of line loss prediction, existing studies mainly focus on mining the complex mapping relationships between various influencing factors and line loss through prediction models (Peng et al, 2021). The whole prediction process can be divided into data preprocessing, model construction, and evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

et al. 2022

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Line loss prediction of ultrahigh voltage transmission lines is the key for ensuring the safe, reliable, and economical operation of the power system. However, the strong volatility of line loss brings challenges to the prediction of transmission line loss. For more accurate prediction, this article uses ensemble empirical mode decomposition (EEMD) to decompose the line loss and proposes the EEMD–LSTM–SVR prediction model. First of all, this article performs feature engineering on power flow, electric energy, and meteorological data and extracts the exponentially weighted moving average (EWMA) feature from the line loss. After the integration of the time dimension, this article mines the curve characteristics from the time series and constructs a multidimensional input dataset. Then, through ensemble empirical mode decomposition, the line loss is decomposed into high-frequency, low-frequency, and random IMFs. These IMFs and the standardized multidimensional dataset together constitute the final input dataset. In this article, each IMF fusion dataset is sent to LSTM and support vector regression models for training. In the training process, the incremental cross-validation method is used for model evaluation, and the grid search method is used for hyperparameter optimization. After evaluation, the LSTM algorithm predicts high-frequency IMF1 and 2 and random IMF4 and 5; the SVR algorithm predicts low-frequency IMF6 and 7 and random IMF3. Finally, the output value of each model is superimposed to obtain the final line loss prediction value. Also, the comparative predictions were performed using EEMD–LSTM, EEMD–SVR, LSTM, and SVR. Compared with the independent prediction models EEMD–LSTM and EEMD–SVR, the combined EEMD–LSTM–SVR algorithm has a decrease in mean absolute performance error% by 2.2 and 25.37, respectively, which fully demonstrates that the combined model has better prediction effect than the individual models. Compared with that of SVR, the MAPE% of EEMD–SVR decreases by 11.16. Compared with that of LSTM, the MAPE% of EEMD–LSTM is reduced by 32.72. The results show that EEMD decomposition of line loss series can effectively improve the prediction accuracy and reduce the strong volatility of line loss. Compared with that of the other four algorithms, EEMD–LSTM–SVR has the highest R-square of 0.9878. Therefore, the algorithm proposed in this article has the best effectiveness, accuracy, and robustness.

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A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy

Cited by 17 publications

References 42 publications

Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm

Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm

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

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

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