Lu Yuefeng, Xu Shijin, Liu Yadong, Jiang Xiuchen
Smart Grid Center, Shanghai Jiao Tong University, Shanghai, China.
Beijing Research Institute, China Southern Power Grid, Beijing, China.
Sci Rep. 2025 Jul 29;15(1):27669. doi: 10.1038/s41598-025-11403-1.
Non-Intrusive Load Monitoring (NILM) estimates load-specific power by disaggregating household-level power data, enabling smart grids to provide more accurate power estimations and thus prevent energy waste and casualties. Some existing NILM methods employ federated learning (FL) with generative models to estimate load power; however, their accuracy often suffers within an FL architecture. This is because the generators tend to learn the most common load patterns while neglecting the less frequent ones. To address this, we propose an FL architecture with a Wasserstein generative adversarial network (FL-WGAN) to enhance accuracy. In our method, each client trains its own generative neural network to estimate load power, while a discriminator network evaluates these estimates. Each client employs a Wasserstein distance-based guidance mechanism to ensure the generative model learns the full distribution of all states rather than being confined to a subset. Additionally, an attention mechanism is integrated into the generative model to further improve its representational capability. We evaluate FL-WGAN using the UA-DALE and REDD datasets, and the results demonstrate that our method outperforms existing methods.
非侵入式负载监测（NILM）通过分解家庭层面的电力数据来估计特定负载的功率，使智能电网能够提供更准确的功率估计，从而防止能源浪费和人员伤亡。一些现有的NILM方法采用带有生成模型的联邦学习（FL）来估计负载功率；然而，在FL架构中，它们的准确性往往会受到影响。这是因为生成器倾向于学习最常见的负载模式，而忽略了不太频繁出现的模式。为了解决这个问题，我们提出了一种带有瓦瑟斯坦生成对抗网络的FL架构（FL-WGAN）来提高准确性。在我们的方法中，每个客户端训练自己的生成神经网络来估计负载功率，而判别器网络则评估这些估计值。每个客户端采用基于瓦瑟斯坦距离的引导机制，以确保生成模型学习所有状态的完整分布，而不是局限于一个子集。此外，注意力机制被集成到生成模型中，以进一步提高其表征能力。我们使用UA-DALE和REDD数据集对FL-WGAN进行评估，结果表明我们的方法优于现有方法。
