In smart home services, non-intrusive load monitoring (NILM) can reveal individual appliances’ power consumption from the aggregate power and requires only one measurement point at the entrance by a smart meter. Most of the existing load disaggregation methods are based on deep and complex neural networks, and excessively long input sequences could increase the model disaggregation time. Meanwhile, constructing representative features and designing effective disaggregation model is becoming increasingly important. Therefore, we utilize a gramian summation difference angular field (GASDF) image, taking any two power sample points’ temporal correlations as input to our baseline model, to better recognize different appliances from the aggregate power sequence. Then, since GASDF could not provide statistical characteristics, we further build the expert feature encoder (EFE) to realize the multi-dimensional representation of power by encoding both current aggregate power and statistical characteristics from historical data as prior knowledge. Afterwards, a batch-normalization (BN)-based normalization fusion (NF) method is proposed to lower the disaggregation error incurred by the distribution difference between GASDF and prior knowledge. Finally, to verify the proposed method’s effectiveness, named ApplianceFilter, experiments are conducted on the UK-DALE and REDD data, showing that load disaggregation is improved using prior knowledge fusion, superior to the existing end-to-end neural network model.

中文翻译：

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ApplianceFilter：通过先验知识融合进行有针对性的电器分解

在智能家居服务中，非侵入式负载监控（NILM）可以从总功率中揭示单个设备的功耗，并且只需要在入口处通过智能电表建立一个测量点。现有的负载分解方法大多数基于深度且复杂的神经网络，过长的输入序列可能会增加模型分解时间。同时，构建代表性特征并设计有效的分解模型变得越来越重要。因此，我们利用格拉米亚求和差异角场（GASDF）图像，将任意两个功率样本点的时间相关性作为基线模型的输入，以更好地从聚合功率序列中识别不同的电器。然后，由于GASDF无法提供统计特征，我们进一步构建专家特征编码器（EFE），通过将当前总功率和历史数据的统计特征编码为先验知识来实现​​功率的多维表示。随后，提出了一种基于批量归一化（BN）的归一化融合（NF）方法来降低由GASDF和先验知识之间的分布差异引起的分解误差。最后，为了验证所提出的方法（名为ApplianceFilter）的有效性，在UK-DALE和REDD数据上进行了实验，结果表明，利用先验知识融合改进了负载分解，优于现有的端到端神经网络模型。