  Residential Load Profile (RLP) generation and prediction are critical for the
operation and planning of distribution networks, especially as diverse
low-carbon technologies (e.g., photovoltaic and electric vehicles) are
increasingly adopted. This paper introduces a novel flow-based generative
model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely
designed for both conditional and unconditional RLP generation, and for
probabilistic load forecasting. By introducing two new layers--the invertible
linear layer and the invertible normalization layer--the proposed FCPFlow
architecture shows three main advantages compared to traditional statistical
and contemporary deep generative models: 1) it is well-suited for RLP
generation under continuous conditions, such as varying weather and annual
electricity consumption, 2) it demonstrates superior scalability in different
datasets compared to traditional statistical models, and 3) it also
demonstrates better modeling capabilities in capturing the complex correlation
of RLPs compared with deep generative models.
