Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam st...Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.展开更多
The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling.The gap parameter is regarded as an effective tool to estimate the ground loss of tunnel...The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling.The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil.More specifically,x,which is a gap parameter component defined as the over(or insufficient)excavation due to the change in the posture of the shield machine,may contribute more to the uncertainty of the ground loss.However,the existing uncertainty characterization methods for x have several limitations and cannot explain the uncertain correlations between the relevant parameters.Along these lines,to better characterize the uncertainty of x,the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it.To attain this goal,1523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction,stratigraphic,and posture parameters were collected to form the database.According to the variance filter method,the mutual information method,and the value of the correlation coefficients,the original 44 parameters were reduced to 10 main parameters,which were unit weight,the stoke of the jacks(A,B,C,and D groups),the pressure of the pushing jacks(A,C groups),the chamber pressure,the rotation speed,and the total force.The multivariate probability distribution was constructed based on the Johnson system of distributions.Moreover,the distribution was satisfactorily verified in explaining the pairwise correlation between x and other parameters through 2 million simulation cases.At last,the distribution was used as a prior distribution to update the marginal distribution of x with any group of the relevant parameters known.The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.展开更多
针对风速的不确定性、时变和非线性特征,提出一种用于风速预测的基于受限玻尔兹曼机和粗糙集理论的区间概率分布学习(Interval Probability Distribution Learning, IPDL)模型。该模型包含一组区间隐藏变量,利用Gibbs抽样和对比散度来...针对风速的不确定性、时变和非线性特征,提出一种用于风速预测的基于受限玻尔兹曼机和粗糙集理论的区间概率分布学习(Interval Probability Distribution Learning, IPDL)模型。该模型包含一组区间隐藏变量,利用Gibbs抽样和对比散度来获取风速的概率分布,结合模糊Ⅱ型推理系统(Fuzzy Type Ⅱ Inference System, FT2IS),设计一个有监督回归的实值区间深度置信网络(Interval Deep Belief Network, IDBN)。算例结果表明,该方法结合了IPDL和FT2IS的鲁棒性,风速预测性能较好。展开更多
基金Supported by the National Natural Science Foundation of China(51777015)the Research Foundation of Education Bureau of Hunan Province(20A021).
文摘Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is proposed.With the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 hours.The presented method can obtain more useful information than conventional point and interval predictions.Moreover,a prediction of the future complete probability distribution of wind power can be realized.According to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind power.Compared with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.
基金support provided by the National Natural Science Foundation of China(Grant Nos.52078236 and 52122806)Guangzhou Metro Group Co.,Ltd(JT204-100111-23001)Chongqing Urban Investment Infrastructure Construction Co(Grant No.CQCT-JS-SC-GC-2022-0081).
文摘The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling.The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil.More specifically,x,which is a gap parameter component defined as the over(or insufficient)excavation due to the change in the posture of the shield machine,may contribute more to the uncertainty of the ground loss.However,the existing uncertainty characterization methods for x have several limitations and cannot explain the uncertain correlations between the relevant parameters.Along these lines,to better characterize the uncertainty of x,the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it.To attain this goal,1523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction,stratigraphic,and posture parameters were collected to form the database.According to the variance filter method,the mutual information method,and the value of the correlation coefficients,the original 44 parameters were reduced to 10 main parameters,which were unit weight,the stoke of the jacks(A,B,C,and D groups),the pressure of the pushing jacks(A,C groups),the chamber pressure,the rotation speed,and the total force.The multivariate probability distribution was constructed based on the Johnson system of distributions.Moreover,the distribution was satisfactorily verified in explaining the pairwise correlation between x and other parameters through 2 million simulation cases.At last,the distribution was used as a prior distribution to update the marginal distribution of x with any group of the relevant parameters known.The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.
文摘针对风速的不确定性、时变和非线性特征,提出一种用于风速预测的基于受限玻尔兹曼机和粗糙集理论的区间概率分布学习(Interval Probability Distribution Learning, IPDL)模型。该模型包含一组区间隐藏变量,利用Gibbs抽样和对比散度来获取风速的概率分布,结合模糊Ⅱ型推理系统(Fuzzy Type Ⅱ Inference System, FT2IS),设计一个有监督回归的实值区间深度置信网络(Interval Deep Belief Network, IDBN)。算例结果表明,该方法结合了IPDL和FT2IS的鲁棒性,风速预测性能较好。
