Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power i...Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.展开更多
为了提高卷烟销售预测准确性,平衡生产与需求,协同工商业,建立切实合理的月供应计划,提出了一个基于ARMA(autoregressive moving average model,自回归滑动平均模型)的混合卷烟销售预测模型,实现卷烟月总量的预测。该模型首先基于ARMA...为了提高卷烟销售预测准确性,平衡生产与需求,协同工商业,建立切实合理的月供应计划,提出了一个基于ARMA(autoregressive moving average model,自回归滑动平均模型)的混合卷烟销售预测模型,实现卷烟月总量的预测。该模型首先基于ARMA建立月预测模型;再用计划评审技术PERT得到月预测经验期望值;最后通过设定加权系数,综合两个预测值得到月预测销售总量。实验结果证明该模型能够较好地预测出规格卷烟月销售总量值变化。展开更多
基金supported by the National Natural Science Foundation of China (No. 51507141)the National Key Research and Development Program of China (No. 2016YFC0401409)the Shaanxi provincial education office fund (No. 17JK0547)
文摘Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.
文摘为了提高卷烟销售预测准确性,平衡生产与需求,协同工商业,建立切实合理的月供应计划,提出了一个基于ARMA(autoregressive moving average model,自回归滑动平均模型)的混合卷烟销售预测模型,实现卷烟月总量的预测。该模型首先基于ARMA建立月预测模型;再用计划评审技术PERT得到月预测经验期望值;最后通过设定加权系数,综合两个预测值得到月预测销售总量。实验结果证明该模型能够较好地预测出规格卷烟月销售总量值变化。
