Prediction of Mianyang Carbon Emission Trend Based on Adaptive GRU Neural Network

Yang, Fei; Liu, Die; Zhang, Qiao; Chen, Zhijun; Ye, Yu; Yang, Yongzhen; He, Yang; Zhou, Shunhang; Zheng, Li

doi:10.1109/icftic57696.2022.10075164

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…This makes RNN very suitable for tasks such as power load forecasting, power system state estimation, and voltage fluctuation prediction. The RNN model can capture the seasonality, periodicity and instability in the power system, so it plays an important role in the optimization of distributed power carrying capacity and grid stability analysis (Yang et al, 2022a). However, RNN models also have some limitations.…”

Section: Introductionmentioning

confidence: 99%

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Yang,

Min

et al. 2024

Front. Energy Res.

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The booming development of distributed power sources in power systems has drawn attention to the carrying capacity and stability of the power grid, becoming a key challenge for the power industry. This study aims to develop a comprehensive deep learning model by combining deep recurrent double Q network (DR-DQN) and deep convolutional neural network (DCNN), and use meta-learning to optimize the model as a whole to simultaneously optimize the power grid. Distributed power supply carrying capacity and predicting the voltage fluctuations of the grid. The comprehensive model is designed to consider distributed power capacity optimization and voltage fluctuation prediction holistically. Through the DR-DQN model, the maximum distributed power capacity is determined under different grid conditions and the distributed power configuration of the grid is optimized. At the same time, the DCNN model is used to analyze the power grid time series data and predict the voltage fluctuation of the power grid. The results are presented in graph form, showing trends in maximum capacity and voltage fluctuations under different grid conditions. Experimental results show that the overall model achieves satisfactory results in distributed power capacity optimization and voltage fluctuation prediction. Performance evaluation and comparison highlight the comprehensive model’s excellent performance in terms of prediction accuracy and computational efficiency, providing new possibilities for efficient management and reliable operation of power systems. The successful development of the model provides practical and reliable solutions for the future development of power systems.

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Section: Introductionmentioning

confidence: 99%

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Yang,

Min

et al. 2024

Front. Energy Res.

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CGAOA-AttBiGRU: A Novel Deep Learning Framework for Forecasting CO2 Emissions

Liu,

Wu,

Tan

et al. 2024

Mathematics

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Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) models, which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. To address these issues, this paper proposes a framework for predicting CO2 emissions, called CGAOA-AttBiGRU. In this framework, Attentional-Bidirectional Gate Recurrent Unit (AttBiGRU) is a prediction model that uses BiGRU units to extract bidirectional temporal features from the data, and adopts an attention mechanism to adaptively weight the bidirectional temporal features, thereby improving prediction accuracy. CGAOA is an improved Arithmetic Optimization Algorithm (AOA) used to optimize the five key hyperparameters of the AttBiGRU. We first validated the optimization performance of the improved CGAOA algorithm on 24 benchmark functions. Then, CGAOA was used to optimize AttBiGRU and compared with 12 optimization algorithms. The results indicate that the AttBiGRU optimized by CGAOA has the best predictive performance.

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CNN-GRU-Attention Neural Networks for Carbon Emission Prediction of Transportation in Jiangsu Province

Wu,

Chen,

Zhao

et al. 2024

Sustainability

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With the increasing energy use and carbon emissions in the transportation industry, its impact on the greenhouse effect is gradually being recognized. Therefore, this study aims to explore the achievement of carbon emission peak and carbon neutrality in transportation through prediction. The research employs a deep learning model, the CNN-GRU-Attention model, to predict carbon emissions in the transportation industry in Jiangsu, China. We select influencing factors through an extended STIRPAT model coupled with Lasso regression, and construct the CNN-GRU-Attention traffic carbon emission prediction model according to data indicators from 1995 to 2021. The model predicts carbon emissions from the transportation industry in Jiangsu Province between 2022 and 2035 under six distinct scenarios and proposes corresponding emission reduction strategies. The results show that the model in this study has higher prediction accuracy compared with other models, with a mean absolute error (MAE) of 0.061582, root mean square error (RMSE) of 0.085025, and R2 of 0.91609 on the test set. Scenario-based predictions reveal that emission peak in the transportation industry in Jiangsu Province can be achieved under the clean development and comprehensive low-carbon scenarios, with technological innovation being the primary driver of low-carbon emission reductions. This study provides a novel approach for forecasting carbon emissions from the transportation industry and explores the implementation path of emission peak through this method.

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Prediction of Mianyang Carbon Emission Trend Based on Adaptive GRU Neural Network

Cited by 3 publications

References 2 publications

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

CGAOA-AttBiGRU: A Novel Deep Learning Framework for Forecasting CO2 Emissions

CNN-GRU-Attention Neural Networks for Carbon Emission Prediction of Transportation in Jiangsu Province

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