A Hybrid Deep Learning Method Based on CEEMDAN and Attention Mechanism for Network Traffic Prediction

Wang, Dong; Bao, Yu-Yang; Wang, Chuan-Mei

doi:10.1109/access.2023.3268437

Cited by 7 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the model framework and parameters are set up mainly based on the literature 28 – 30 and adjusted through multiple experimental tests and personal experience. The TCN framework composed of 5 stacked TCN-ECA modules is used in this experiment.…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model

Xiang,

Li,

Zhang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R2). The best RMSE, MAE and R2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models.

show abstract

Section: Data Sources and Preprocessingmentioning

confidence: 99%

A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model

Xiang,

Li,

Zhang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

A deep learning based communication traffic prediction approach for smart monitoring of distributed energy resources in virtual power plants

Hou,

Liu,

Zheng

et al. 2024

IET Smart Grid

View full text Add to dashboard Cite

Virtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimension in depicting the behaviour characteristics of distributed energy resources in VPPs. Therefore, the prediction of communication traffic is significant in improving the control efficiency of VPPs. However, due to the involvement of numerous interactive agents characterised by both individual randomness and coordinative characteristics, traditional prediction models are no longer capable of fitting VPP communication traffic effectively. Therefore, a novel prediction model is introduced that enhances the prediction accuracy by integrating long short‐term memory (LSTM) and variational mode decomposition (VMD). This model employs VMD as the initial step for extracting the intrinsic modes from the traffic sequence, thereby mitigating the impact of incidental noise. Then, LSTM is applied to fit each intrinsic mode individually. Additionally, considering the outer influencing factors, the attention mechanism is incorporated. Finally, all sub‐prediction algorithms are neatly integrated as a whole prediction model. The proposed model is evaluated through simulation prediction using realistic VPP communication traffic data, and the results demonstrate its effectiveness.

show abstract

Attention to Traffic: Network Traffic Classification using Attention-Based CNNs

Anwar,

Agrawal,

Saroha

et al. 2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

View full text Add to dashboard Cite

A Hybrid Deep Learning Method Based on CEEMDAN and Attention Mechanism for Network Traffic Prediction

Cited by 7 publications

References 44 publications

A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model

A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model

A deep learning based communication traffic prediction approach for smart monitoring of distributed energy resources in virtual power plants

Attention to Traffic: Network Traffic Classification using Attention-Based CNNs

Contact Info

Product

Resources

About