Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, particularly as diverse low-carbon technologies are increasingly integrated. 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 shows superior scalability in different datasets compared to traditional statistical, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models.

翻译：居民负荷曲线（RLP）的生成与预测对于配电网的运行和规划至关重要，尤其是在多种低碳技术不断融合的背景下。本文提出了一种新颖的基于流的生成模型——全卷积流模型（FCPFlow），该模型专门设计用于条件性和无条件性RLP生成，以及概率性负荷预测。通过引入两个新层——可逆线性层和可逆归一化层——所提出的FCPFlow架构相较于传统统计模型和当代深度生成模型展现出三个主要优势：1）它非常适用于连续条件下的RLP生成，例如变化的天气和年度用电量；2）与传统统计模型相比，它在不同数据集上表现出优越的可扩展性；3）与深度生成模型相比，它在捕捉RLP复杂相关性方面展现出更强的建模能力。