The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to elimin...The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.展开更多
基金supported by the National Natural Science Foundation of China(71071077)the Ministry of Education Key Project of National Educational Science Planning(DFA090215)+1 种基金China Postdoctoral Science Foundation(20100481137)Funding of Jiangsu Innovation Program for Graduate Education(CXZZ11-0226)
文摘The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.
文摘准确预测台区的电力负荷,能够促使电力企业合理安排调度计划,保障台区电力安全和经济稳定运行。为了充分挖掘电力负荷数据的特征,提高预测的精度,提出一种基于自适应辛几何模态分解(adaptive symplectic geometry mode decomposition,ASGMD)、多元线性回归(multiple linear regression,MLR)和卷积长短时记忆(convolutional long short-term memory,CLSTM)网络的电力负荷预测方法。首先,应用ASGMD将台区负荷数据分解为弱相关和强相关两种分量;然后,利用MLR和CLSTM分别对上述两种分量分别进行预测;最后,组合各模型结果,得到最终负荷预测值。实例分析结果表明,所提模型较其他模型具有更高的预测准确度。
