CAG：聚类聚合 GAN 生成家电用电模式 Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation

精读摘要

合成家电用电数据对非侵入式负荷监测算法开发和隐私保护型能源研究至关重要，但标注数据稀缺是主要障碍。现有 GAN 方法已能合成负荷模式，却把间歇性与连续性电器混在一个模型里统一处理，忽视行为差异，导致训练不稳定、保真度有限。CAG 按聚类分组建模不同电器类型，以混合模型提升生成质量，为智能电表数据分析提供更可靠的合成数据。 Synthetic appliance data are essential for non-intrusive load monitoring and privacy-preserving energy research, yet labeled datasets are scarce. Existing GAN-based methods treat all devices uniformly in a single model, neglecting behavioral differences between intermittent and continuous appliances and causing unstable training and limited fidelity. CAG is a cluster-based hybrid model that generates appliance patterns by device group.