By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) for...By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.展开更多
Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department t...Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.展开更多
The long-term energy demand in China and the-Chinese share in global CO2 emission are forecasted on the basis of scenarios of population growth and economy development up to 2050 proposed in view of the interaction of...The long-term energy demand in China and the-Chinese share in global CO2 emission are forecasted on the basis of scenarios of population growth and economy development up to 2050 proposed in view of the interaction of energy, economy, environment and social development. The total energy demand in 2050 will reach 4.4~ 5.4 billion tce. It is shown in energy supply analysis that coal is China’s major energy in primary energy supply. The share of CO2 emission in the future Chinese energy system will be out of proportion to its energy consumption share because of the high persentage of coal to be consumed. It will reach about 27%. The nuclear option which would replace 30.7% of coal in the total primary energy supply will reduce the share by 9.8%. So the policy considerations on the future Chinese energy system is of great importance to the global CO2 issues.展开更多
基于数据驱动的方法已广泛应用于电力负荷预测领域,以提升预测精度。然而,当售电公司接入新用户时,由于缺乏用户历史用电数据,常规数据驱动方法的适用性会受到一定限制。为解决这一问题,文章提出了一种基于域对抗迁移网络(domain advers...基于数据驱动的方法已广泛应用于电力负荷预测领域,以提升预测精度。然而,当售电公司接入新用户时,由于缺乏用户历史用电数据,常规数据驱动方法的适用性会受到一定限制。为解决这一问题,文章提出了一种基于域对抗迁移网络(domain adversarial transfer network,DATN)的短期电力负荷预测方法。该模型利用Transformer模型作为特征提取器,以捕捉负荷数据中的动态特征和时间依赖性。随后,负荷预测器基于这些特征精准预测未来的负荷情况。通过域判别器与特征提取器的对抗学习,确保模型能够学习到深层域不变特征,同时结合多核最大均值差异(multi-kernel maximum mean discrepancy,MK-MMD)和相关性对齐(correlation alignment,CORAL)进一步减小源域与目标域数据的分布差异。所提模型在南方某省工业用户的用电数据上进行了验证,实验结果表明,在小样本场景下,该方法具备较好的预测精度和场景适应性。展开更多
基金Supported by National High Technology Research and Development Program of China （863 Program） （2007AA11Z221）, International Cooperation Project of Shanghai （08210707500）, and Natural Science Foundation of Shanghai.（08ZR1420600） . _
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.
基金Project(61873283)supported by the National Natural Science Foundation of ChinaProject(KQ1707017)supported by the Changsha Science&Technology Project,ChinaProject(2019CX005)supported by the Innovation Driven Project of the Central South University,China。
文摘Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.
文摘The long-term energy demand in China and the-Chinese share in global CO2 emission are forecasted on the basis of scenarios of population growth and economy development up to 2050 proposed in view of the interaction of energy, economy, environment and social development. The total energy demand in 2050 will reach 4.4~ 5.4 billion tce. It is shown in energy supply analysis that coal is China’s major energy in primary energy supply. The share of CO2 emission in the future Chinese energy system will be out of proportion to its energy consumption share because of the high persentage of coal to be consumed. It will reach about 27%. The nuclear option which would replace 30.7% of coal in the total primary energy supply will reduce the share by 9.8%. So the policy considerations on the future Chinese energy system is of great importance to the global CO2 issues.
文摘基于数据驱动的方法已广泛应用于电力负荷预测领域,以提升预测精度。然而,当售电公司接入新用户时,由于缺乏用户历史用电数据,常规数据驱动方法的适用性会受到一定限制。为解决这一问题,文章提出了一种基于域对抗迁移网络(domain adversarial transfer network,DATN)的短期电力负荷预测方法。该模型利用Transformer模型作为特征提取器,以捕捉负荷数据中的动态特征和时间依赖性。随后,负荷预测器基于这些特征精准预测未来的负荷情况。通过域判别器与特征提取器的对抗学习,确保模型能够学习到深层域不变特征,同时结合多核最大均值差异(multi-kernel maximum mean discrepancy,MK-MMD)和相关性对齐(correlation alignment,CORAL)进一步减小源域与目标域数据的分布差异。所提模型在南方某省工业用户的用电数据上进行了验证,实验结果表明,在小样本场景下,该方法具备较好的预测精度和场景适应性。
