Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
With the European Union(EU)introducing the Carbon Border Adjustment Mechanism(CBAM),accurately forecasting EU carbon price is crucial for exporters to estimate export costs,plan low-carbon strategies,and mitigate trad...With the European Union(EU)introducing the Carbon Border Adjustment Mechanism(CBAM),accurately forecasting EU carbon price is crucial for exporters to estimate export costs,plan low-carbon strategies,and mitigate trade risks.In the petroleum sector,carbon pricing directly influences upstream investment returns and carbon intensity targets,thereby closely linking emissions markets with fossil energy strategies.Existing models often fail to fully capture the nonlinear,non-stationary nature of carbon prices and their dependence on external factors.This study proposes a novel hybrid framework that combines improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)with gated recurrent unit-convolutional neural network-long short-term memory network-Bayesian optimization(GRU-CNN-LSTM-BO).Empirical results based on the EU emissions trading system(ETS)market demonstrate that the proposed model significantly improves forecasting accuracy.Among all experiments,the proposed GRU-CNN-LSTM-BO framework achieves the best performance,yielding the lowest MAE(1.3872),RMSE(1.7038),MAPE(0.0166),and MSPE(0.0004),as well as the highest R2(0.9400).Compared to all benchmark models,the GRU-CNN-LSTM-BO model achieves reductions in MAE and RMSE ranging from 5.38%to 63.65%and 8.97%to 64.41%,respectively.To further validate the generalization ability and predictive performance of the proposed model,it is also applied to China's ETS.The results show that the GRU-CNN-LSTM-BO model also performs very well in China's ETS.展开更多
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
基金supported by the National Natural Science Foundation of China(Grant No.72401011).
文摘With the European Union(EU)introducing the Carbon Border Adjustment Mechanism(CBAM),accurately forecasting EU carbon price is crucial for exporters to estimate export costs,plan low-carbon strategies,and mitigate trade risks.In the petroleum sector,carbon pricing directly influences upstream investment returns and carbon intensity targets,thereby closely linking emissions markets with fossil energy strategies.Existing models often fail to fully capture the nonlinear,non-stationary nature of carbon prices and their dependence on external factors.This study proposes a novel hybrid framework that combines improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)with gated recurrent unit-convolutional neural network-long short-term memory network-Bayesian optimization(GRU-CNN-LSTM-BO).Empirical results based on the EU emissions trading system(ETS)market demonstrate that the proposed model significantly improves forecasting accuracy.Among all experiments,the proposed GRU-CNN-LSTM-BO framework achieves the best performance,yielding the lowest MAE(1.3872),RMSE(1.7038),MAPE(0.0166),and MSPE(0.0004),as well as the highest R2(0.9400).Compared to all benchmark models,the GRU-CNN-LSTM-BO model achieves reductions in MAE and RMSE ranging from 5.38%to 63.65%and 8.97%to 64.41%,respectively.To further validate the generalization ability and predictive performance of the proposed model,it is also applied to China's ETS.The results show that the GRU-CNN-LSTM-BO model also performs very well in China's ETS.
