In this paper, we present the problem formulation and methodology framework of Super Resolution Perception (SRP) on smart meter data. With the widespread use of smart meters, a massive amount of electricity consumption data can be obtained. Smart meter data is the basis of automated billing and pricing, appliance identification, demand response, etc. However, the provision of high-quality data may be expensive in many cases. In this paper, we propose a novel problem - the SRP problem as reconstructing high-quality data from unsatisfactory data in smart grids. Advanced generative models are then proposed to solve the problem. This technology makes it possible for empowering existing facilities without upgrading existing meters or deploying additional meters. We first mathematically formulate the SRP problem under the Maximum a Posteriori (MAP) estimation framework. The dataset namely Super Resolution Perception Dataset (SRPD) is designed for this problem and released. A case study is then presented, which performs SRP on smart meter data. A network namely Super Resolution Perception Convolutional Neural Network (SRPCNN) is proposed to generate high-frequency load data from low-frequency data. Experiments demonstrate that our SRP models can reconstruct high-frequency data effectively. Moreover, the reconstructed high-frequency data can lead to better appliance identification results.

在本文中，我们介绍了智能电表数据的超分辨率感知（SRP）问题表述和方法框架。随着智能电表的广泛使用，可以获得大量的用电量数据。智能电表数据是自动计费和定价，设备标识，需求响应等的基础。但是，在许多情况下提供高质量数据可能会很昂贵。在本文中，我们提出了一个新问题-SRP问题，即从智能电网中的不满意数据中重建高质量数据。然后提出高级生成模型来解决该问题。这项技术可以在不升级现有仪表或部署其他仪表的情况下授权现有设施。我们首先在最大后验（MAP）估计框架下用数学公式表示SRP问题。数据集即超分辨率感知数据集（SRPD）是针对此问题而设计的，并已发布。然后介绍了一个案例研究，该案例对智能电表数据执行SRP。提出了一种网络，即超分辨率感知卷积神经网络（SRPCNN），以从低频数据生成高频负载数据。实验表明，我们的SRP模型可以有效地重建高频数据。此外，重构的高频数据可以带来更好的设备识别结果。提出了一种网络，即超分辨率感知卷积神经网络（SRPCNN），以从低频数据生成高频负载数据。实验表明，我们的SRP模型可以有效地重建高频数据。此外，重构的高频数据可以带来更好的设备识别结果。提出了一种网络，即超分辨率感知卷积神经网络（SRPCNN），以从低频数据生成高频负载数据。实验表明，我们的SRP模型可以有效地重建高频数据。此外，重构的高频数据可以带来更好的设备识别结果。