Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and building applications. However, the increasing adoption of behind-the-meter (BTM) energy sources such as solar panels and battery storage poses new challenges for conventional NILM methods that rely solely on at-the-meter data. The energy injected from the BTM sources can obscure the power signatures of individual appliances, leading to a significant decrease in NILM performance. To address this challenge, we present DualNILM, a deep multi-task learning framework designed for the dual tasks of appliance state recognition and injected energy identification. Using a Transformer-based architecture that integrates sequence-to-point and sequence-to-sequence strategies, DualNILM effectively captures multiscale temporal dependencies in the aggregate power consumption patterns, allowing for accurate appliance state recognition and energy injection identification. Extensive evaluation on self-collected and synthesized datasets demonstrates that DualNILM maintains an excellent performance for dual tasks in NILM, much outperforming conventional methods. Our work underscores the framework's potential for robust energy disaggregation in modern energy systems with renewable penetration. Synthetic photovoltaic augmented datasets with realistic injection simulation methodology are open-sourced at https://github.com/MathAdventurer/PV-Augmented-NILM-Datasets.

翻译：非侵入式负荷监测（NILM）为智能家居和建筑应用提供了一种经济高效的方法，以获取细粒度的设备级能耗数据。然而，太阳能电池板和电池储能等表后能源的日益普及，对仅依赖表端数据的传统NILM方法提出了新的挑战。来自表后能源的能量注入可能掩盖单个电器的功率特征，导致NILM性能显著下降。为应对这一挑战，我们提出了DualNILM——一种专为电器状态识别与注入能量识别双重任务设计的深度多任务学习框架。通过采用融合序列到点与序列到序列策略的Transformer架构，DualNILM能有效捕捉总功耗模式中的多尺度时间依赖性，从而实现精确的电器状态识别与能量注入识别。在自采集数据集与合成数据集上的广泛评估表明，DualNILM在NILM双重任务中保持卓越性能，显著优于传统方法。我们的研究凸显了该框架在具有可再生能源渗透的现代能源系统中实现鲁棒能量分解的潜力。采用真实注入模拟方法合成的光伏增强数据集已在https://github.com/MathAdventurer/PV-Augmented-NILM-Datasets开源。