Non-Intrusive Load Monitoring (NILM) is a computational technique to estimate the power loads' appliance-by-appliance from the whole consumption measured by a single meter. In this paper, we propose a conditional density estimation model, based on deep neural networks, that joins a Conditional Variational Autoencoder with a Conditional Invertible Normalizing Flow model to estimate the individual appliance's power demand. The resulting model is called Prior Flow Variational Autoencoder or, for simplicity PFVAE. Thus, instead of having one model per appliance, the resulting model is responsible for estimating the power demand, appliance-by-appliance, at once. We train and evaluate our proposed model in a publicly available dataset composed of power demand measures from a poultry feed factory located in Brazil. The proposed model's quality is evaluated by comparing the obtained normalized disaggregation error (NDE) and signal aggregated error (SAE) with the previous work values on the same dataset. Our proposal achieves highly competitive results, and for six of the eight machines belonging to the dataset, we observe consistent improvements that go from 28% up to 81% in NDE and from 27% up to 86% in SAE.

中文翻译：

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先验流量变化自动编码器：用于非侵入式负载监测的密度估算模型

非侵入式负载监控（NILM）是一种计算技术，可以根据单个电表测得的总功耗来估算设备的用电设备。在本文中，我们提出了一个基于深度神经网络的条件密度估计模型，该模型将条件变分自编码器与条件可逆归一化流量模型结合在一起，以估计单个设备的功率需求。所得模型称为“先验流量变化自动编码器”，或简称为PFVAE。因此，代替每个设备具有一个模型，所得模型负责一次估计每个设备的功率需求。我们在公开的数据集中训练和评估我们提出的模型，该数据集由位于巴西的家禽饲料工厂的电力需求量构成。提议的模型” 通过将获得的归一化分类误差（NDE）和信号聚合误差（SAE）与同一数据集上的先前工作值进行比较，来评估s的质量。我们的建议取得了极具竞争力的结果，对于属于数据集的八台机器中的六台，我们观察到持续改进，NDE从28％提高到81％，SAE从27％提高到86％。