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.

翻译：居民负荷曲线（RLP）的生成与预测对于配电网运行与规划至关重要，尤其在光伏、电动汽车等多样化低碳技术日益普及的背景下。本文提出一种新型的基于流的生成模型——全卷积配置文件流（FCPFlow），该模型独特地适用于有条件和无条件的RLP生成，以及概率负荷预测。通过引入两种新层——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大优势：1）在连续条件（如不同天气与年用电量）下特别适合RLP生成；2）在不同数据集中相较于传统统计模型表现出更优的可扩展性；3）相较于深度生成模型，在捕捉RLP复杂相关性方面展现出更强的建模能力。