Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

Figure 3

A simple instance of a multiscale one-dimensional causal convolution block. This block includes four multiscale convolutional layers with the filter size of 2, resulting in a receptive field size of 16 for a top-layer node. “Causal” means that each hidden node in this network only obtains information about the past. The dilation rate is 1, 2, 4, and 8 from the bottom to top. The first convolutional layer with dilation rate 1 is an ordinary causal convolutional layer, and other layers with dilation rate greater than 1 could fuse features of multiple time scales.