School of Electrical Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2023 Mar 28;23(7):3540. doi: 10.3390/s23073540.
Non-intrusive Load Monitoring (NILM) is a critical technology that enables detailed analysis of household energy consumption without requiring individual metering of every appliance, and has the capability to provide valuable insights into energy usage behavior, facilitate energy conservation, and optimize load management. Currently, deep learning models have been widely adopted as state-of-the-art approaches for NILM. In this study, we introduce DiffNILM, a novel energy disaggregation framework that utilizes diffusion probabilistic models to distinguish power consumption patterns of individual appliances from aggregated power. Starting from a random Gaussian noise, the target waveform is iteratively reconstructed via a sampler conditioned on the total active power and encoded temporal features. The proposed method is evaluated on two public datasets, REDD and UKDALE. The results demonstrated that DiffNILM outperforms baseline models on several key metrics on both datasets and shows a remarkable ability to effectively recreate complex load signatures. The study highlights the potential of diffusion models to advance the field of NILM and presents a promising approach for future energy disaggregation research.
非侵入式负载监测 (NILM) 是一项关键技术，它能够在不要求对每个电器进行单独计量的情况下，对家庭能源消耗进行详细分析，并具有提供能源使用行为的有价值见解、促进节能和优化负载管理的能力。目前，深度学习模型已被广泛采用作为 NILM 的最新方法。在这项研究中，我们引入了 DiffNILM，这是一种新颖的能源分解框架，利用扩散概率模型从总有功功率和编码的时间特征来区分各个电器的功耗模式。从随机高斯噪声开始，通过条件采样器从总有功功率和编码的时间特征对目标波形进行迭代重建。该方法在两个公共数据集 REDD 和 UKDALE 上进行了评估。结果表明，DiffNILM 在两个数据集的几个关键指标上均优于基线模型，并表现出有效重现复杂负载特征的显著能力。该研究强调了扩散模型在推进 NILM 领域的潜力，并为未来的能源分解研究提供了一种有前途的方法。
