Nonintrusive load monitoring (NILM) strives to achieve real-time monitoring of individual appliance energy consumption and usage by leveraging aggregate power readings. Most of the existing NILM models are based on machine learning. To improve the generalization and accuracy of NILM models, it is crucial to expand the dataset. However, collecting a large amount of power data is a challenging and common task. To address this, we propose a novel model called conditional-time-series generative adversarial network (C-TimeGAN), which extends upon TimeGAN by incorporating constraint conditions to generate high-quality and realistic appliance trajectories. This model preserves the benefits of unsupervised GANs’ flexibility and supervised training’s stepwise control in TimeGAN while addressing challenges in appliance data generation, such as rapid power changes and transients. In addition, we utilize a one-class support vector machine (OCSVM) for postprocessing to further enhance data quality by detecting anomalies and removing outliers. Experiments show that the synthetic data from our C-TimeGAN+ model (C-TimeGAN integrated with OCSVM) outperform the baseline in terms of both quality and quantity, exhibiting higher fidelity, diversity, and novelty. Furthermore, C-TimeGAN+ serves as a practical data augmentation tool, enhancing disaggregation accuracy and generalization of other NILM models. We also discuss the optimal proportion of synthetic data for augmentation, improving the practical applicability of the data.

中文翻译：

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用于非侵入式负载监控中真实且高质量设备轨迹生成和数据增强的条件时间 GAN

非侵入式负载监控 (NILM) 致力于通过利用总功率读数来实时监控单个设备的能耗和使用情况。大多数现有的 NILM 模型都是基于机器学习的。为了提高 NILM 模型的泛化性和准确性，扩展数据集至关重要。然而，收集大量电力数据是一项具有挑战性且常见的任务。为了解决这个问题，我们提出了一种称为条件时间序列生成对抗网络（C-TimeGAN）的新模型，它通过合并约束条件来扩展 TimeGAN，以生成高质量且真实的应用轨迹。该模型保留了 TimeGAN 中无监督 GAN 的灵活性和监督训练的逐步控制的优势，同时解决了设备数据生成中的挑战，例如快速功率变化和瞬态。此外，我们利用一类支持向量机（OCSVM）进行后处理，通过检测异常和消除异常值来进一步提高数据质量。实验表明，我们的 C-TimeGAN+ 模型（C-TimeGAN 与 OCSVM 集成）的合成数据在质量和数量上都优于基线，表现出更高的保真度、多样性和新颖性。此外，C-TimeGAN+ 可作为实用的数据增强工具，提高其他 NILM 模型的分解精度和泛化能力。我们还讨论了用于增强的合成数据的最佳比例，提高了数据的实际适用性。