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<p>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.</p>

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Federated learning-enhanced generative models for non-intrusive load monitoring in smart homes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月29日

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  • Yuefeng Lu,
  • Shijin Xu,
  • Yadong Liu,
  • Xiuchen Jiang

摘要

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.