Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research, yet the scarcity of labeled datasets remains a significant barrier. Recent GAN-based methods have demonstrated the feasibility of synthesizing load patterns, but most existing approaches treat all devices uniformly within a single model, neglecting the behavioral differences between intermittent and continuous appliances and resulting in unstable training and limited output fidelity. To address these limitations, we propose the Cluster Aggregated GAN framework, a hybrid generative approach that routes each appliance to a specialized branch based on its behavioral characteristics. For intermittent appliances, a clustering module groups similar activation patterns and allocates dedicated generators for each cluster, ensuring that both common and rare operational modes receive adequate modeling capacity. Continuous appliances follow a separate branch that employs an LSTM-based generator to capture gradual temporal evolution while maintaining training stability through sequence compression. Extensive experiments on the UVIC smart plug dataset demonstrate that the proposed framework consistently outperforms baseline methods across metrics measuring realism, diversity, and training stability, and that integrating clustering as an active generative component substantially improves both interpretability and scalability. These findings establish the proposed framework as an effective approach for synthetic load generation in non-intrusive load monitoring research.

翻译：合成设备数据对于开发非侵入式负荷监测算法和实现隐私保护的能源研究至关重要，然而标记数据集的稀缺性仍是重大障碍。近期基于生成对抗网络的方法已证明合成负荷模式的可行性，但大多数现有方法在单一模型内统一处理所有设备，忽视了间歇性与连续性设备的行为差异，导致训练不稳定且输出逼真度受限。为解决上述局限，我们提出集群聚合生成对抗网络框架，这是一种混合式生成方法，可根据设备行为特性将各设备路由至专门分支。针对间歇性设备，聚类模块将相似的激活模式分组并为每个簇分配专用生成器，确保常见与罕见运行模式均获得充分建模能力。连续性设备则遵循独立分支，采用基于长短期记忆网络的生成器捕捉渐进式时序演化，并通过序列压缩保持训练稳定性。在UVIC智能插头数据集上的大量实验表明，该框架在衡量真实度、多样性和训练稳定性的指标上始终优于基线方法，将聚类作为主动生成组件显著提升了可解释性与可扩展性。这些发现确立了该框架作为非侵入式负荷监测研究中合成负荷生成的有效方法。