Frequency Selective Auto-Encoder for Smart Meter Data Compression

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 4; p. 1521
• Main Authors: Lee, Jihoon, Yoon, Seungwook, Hwang, Euiseok
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22-02-2021; MDPI
• Subjects: auto-encoder; Big Data; Communication; Compression ratio; Consumption; Data compression; Deep learning; digital signal processing; Electric power grids; Energy; Internet of Things; Methods; Principal components analysis; Reconstruction; Residential energy; Signal processing; Situational awareness; smart meter; Smart sensors; Sparsity; Spectra; Visibility; auto-encoder; data compression; smart meter; digital signal processing
• ISSN: 1424-8220; 1424-8220
• 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.