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%.