Lee Jihoon, Yoon Seungwook, Hwang Euiseok
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea.
School of Mechatronics, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea.
Sensors (Basel). 2021 Feb 22;21(4):1521. doi: 10.3390/s21041521.
With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.
随着物联网（IoT）的发展，电网借助大量物联网传感器（如智能电表）实现了智能化。一般来说，安装的智能电表可以收集大量数据，以提高电网的可视性和态势感知能力。然而，有限的存储和通信能力会限制它们在物联网环境中的基础设施建设。为缓解这些问题，需要高效且多样的压缩技术。基于深度学习的压缩技术，如自动编码器（AE），最近已被用于此目的。然而，当高频采样功率数据的频谱特性随时间广泛变化时，现有模型的压缩性能可能会受到限制。本文提出了一种基于频率选择方法的AE压缩模型，该模型在保持压缩率（CR）的同时提高了重建质量。为了实现高效的数据压缩，所提出的方法根据相应时间窗口的频谱特性选择性地应用定制的压缩模型。所提出方法的框架包括两个主要步骤：（i）将功率数据划分为一系列具有特定频谱特性（高频、中频和低频主导）的时间窗口；（ii）分别训练和选择性应用AE模型，使其为最适合每个频率特性的功率数据压缩做好准备。在对荷兰住宅能源数据集的模拟中，频率选择性AE模型在相同CR下显示出比现有模型显著更高的重建性能。此外，所提出的模型降低了学习过程分析中涉及的计算复杂度。
