基于MCQRDDC的负荷概率预测模型

针对具有约束性的复合分位数回归网络(monotone composite quantile regression neural network, MCQRNN)无法较好地分析负荷数据之中的时序信息和内在规律的问题,本研究融合MCQRNN以及膨胀因果卷积网络(dilated causal convolutional networks, DCC),提出了一种新的分位数回归模型MCQRDCC (monotone composite quantile regression dilated causal convolutional networks),该模型将输入划分为分位点输入与非约束输入,使该模型的输出随分位点的增大而增大,以此解决分位数交叉的问题.同时,使用DCC的结构,使该模型充分地分析负荷数据之中的序列信息,使得预测结果更加符合真实负荷的变化趋势.此外, MCQRNN使用指数函数对约束权重矩阵和隐藏层权重进行转化,会影响反向传播时权重的调整,本研究使用ReLU函数代替指数函数可以解决这个问题,以此提高预测的精度.使用真实的负荷数据进行实验,实验结果表明, MCQRDCC能有效地提高预测...

Probabilistic Load Forecasting Model Based on MCQRDDC

Monotone composite quantile regression neural network (MCQRNN) cannot analyze the time series information and internal laws in load data well. In order to address this issue, this study combines MCQRNN and dilated causal convolutional networks (DCC) and proposes a new quantile regression model named, MCQRDCC. This model divides the input into quantile input and unconstrained input to make the output of the model increase with the increase in quantile, so as to solve the problem of quantile crossing. At the same time, the DCC structure is used to help the model fully analyze the sequence information in the load data and make the prediction results more in line with the changing trend of the real load. In addition, MCQRNN utilizes the exponential function to transform the constraint weight matrix and the hidden layer weight, which will affect the weight adjustment during backpropagation. In this study, the ReLU function is used instead of the exponential function to solve this problem and improve the prediction accuracy, and real load data is adopted for experiments. The experimental results show that MCQRDCC can effectively improve prediction accuracy. Compared with those of MCQRNN, the average Pinball loss and CWC of MCQRDCC are decreased by 2.11% and 9.31%, respectively, and AIS is increased by 10.51%.