Condition prediction of submarine cable based on CNN-BiGRU integrating attention mechanism

Yang, Wei; Huang, Bo; Zhang, Anan; Li, Qian; Li, Jiaxing; Xue, Xinghui

doi:10.3389/fenrg.2022.1023822

Cited by 6 publications

(8 citation statements)

References 39 publications

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“…Furthermore, we can compare different variants of the attention module, such as Cross-AM, Multi-Head-AM, and Dynamic-AM. While these models exhibit comparable performance to our Cross-AM (Yang et al, 2022) 148…”

Section: Experimental Results and Analysismentioning

confidence: 82%

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Deep Learning in Carbon Neutrality Forecasting

Ran,

Zou,

Niu

2024

Journal of Organizational and End User Computing

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With the growing urgency of global climate change, carbon neutrality, as a strategy to reduce greenhouse gas emissions into the atmosphere, is increasingly seen as a critical solution. However, current forecasting models still face significant challenges and limitations in accurately and effectively predicting carbon emissions and their associated effects. These challenges largely stem from the complexity of carbon emission data and the interplay of anthropogenic and natural factors. To overcome these obstacles, the authors introduce an advanced forecasting model, the SSA-Attention-BIGRU network. This model ingeniously integrates an external attention mechanism, bidirectional GRU, and SSA components, allowing it to synthesize various key factors and enhance prediction accuracy when forecasting carbon neutrality trends. Through experiments on multiple datasets, the results demonstrate that, compared to other popular methods, the SSA-Attention-BIGRU network significantly excels in prediction accuracy, robustness, and reliability.

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Section: Experimental Results and Analysismentioning

confidence: 82%

“…The Bidirectional Gated Recurrent Unit, commonly known as BiGRU, is an evolution of the standard Gated Recurrent Unit (GRU) (Yang et al, 2022). The GRU was developed to address the vanishing gradient problem in Recurrent Neural Networks (RNNs), aiming to offer an alternative with fewer parameters and higher computational efficiency.…”

Section: Bigru Modelmentioning

confidence: 99%

Deep Learning in Carbon Neutrality Forecasting

Ran,

Zou,

Niu

2024

Journal of Organizational and End User Computing

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“…To capture the deep features of PV power data, the BiGRU network integrates historical and future information seamlessly. Figure 2 illustrates the structure of the BiGRU model [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Guo,

Xu,

Wang

et al. 2024

Sensors

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Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

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“…GRU (Gated Recurrent Unit) is a variant of recurrent neural networks (RNNs) known for its strong sequence modeling capabilities (Lv et al, 2023;Yang et al, 2022). In the GRU-Transformer model, the GRU network plays a crucial role in handling short-term dependencies within sequential data.…”

Section: Gru Modelmentioning

confidence: 99%

ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction

Jiang,

Liu,

et al. 2024

Front. Ecol. Evol.

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IntroductionCarbon neutrality has become a key strategy to combat global climate change. However, current methods for predicting carbon emissions are limited and require the development of more effective strategies to meet this challenge. This is especially true in the field of sports and competitions, where the energy intensity of major events and activities means that time series data is crucial for predicting related carbon emissions, as it can detail the emission patterns over a period of time.MethodIn this study, we introduce an artificial intelligence-based method aimed at improving the accuracy and reliability of carbon emission predictions. Specifically, our model integrates an Improved Mahjong Search Algorithm (ISSA) and GRU-Transformer technology, designed to efficiently process and analyze the complex time series data generated by sporting events. These technological components help to capture and parse carbon emission data more accurately.ResultsExperimental results have demonstrated the efficiency of our model, which underwent a comprehensive evaluation involving multiple datasets and was benchmarked against competing models. Our model outperformed others across various performance metrics, including lower RMSE and MAE values and higher R2 scores. This underscores the significant potential of our model in enhancing the accuracy of carbon emission predictions.DiscussionBy introducing this new AI-based method for predicting carbon emissions, this study not only provides more accurate data support for optimizing and implementing carbon neutrality measures in the sports field but also improves the accuracy of time series data predictions. This enables a deeper understanding of carbon emission trends associated with sports activities. It contributes to the development of more effective mitigation strategies, making a significant contribution to global efforts to reduce carbon emissions.

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Condition prediction of submarine cable based on CNN-BiGRU integrating attention mechanism

Cited by 6 publications

References 39 publications

Deep Learning in Carbon Neutrality Forecasting

Deep Learning in Carbon Neutrality Forecasting

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction

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