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.

介绍了一种新颖的基于流的生成模型FCPFlow，可用于产生条件和无条件的住宅负荷曲线（RLP）和概率负荷预测。该模型通过引入可逆线性层和可逆正则化层来展现出在连续条件下适用、在不同数据集上可扩展性强以及相对于深度生成模型更好地捕捉RLP的复杂相关性等三个主要优点。

基于流量的条件和概率电力消耗配置生成与预测模型