Frequency Selective Auto-Encoder for Smart Meter Data Compression
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 comm...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1521 |
| _version_ | 1827588611051094016 |
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| author | Jihoon Lee Seungwook Yoon Euiseok Hwang |
| author_facet | Jihoon Lee Seungwook Yoon Euiseok Hwang |
| author_sort | Jihoon Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:38:51Z |
| format | Article |
| id | doaj.art-cdbaf218ce2440fc977f05e64ebe8f3f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:38:51Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cdbaf218ce2440fc977f05e64ebe8f3f2023-12-11T18:00:12ZengMDPI AGSensors1424-82202021-02-01214152110.3390/s21041521Frequency Selective Auto-Encoder for Smart Meter Data CompressionJihoon Lee0Seungwook Yoon1Euiseok Hwang2School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, KoreaSchool of Mechatronics, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, KoreaWith 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.https://www.mdpi.com/1424-8220/21/4/1521data compressionsmart meterauto-encoderdigital signal processing |
| spellingShingle | Jihoon Lee Seungwook Yoon Euiseok Hwang Frequency Selective Auto-Encoder for Smart Meter Data Compression Sensors data compression smart meter auto-encoder digital signal processing |
| title | Frequency Selective Auto-Encoder for Smart Meter Data Compression |
| title_full | Frequency Selective Auto-Encoder for Smart Meter Data Compression |
| title_fullStr | Frequency Selective Auto-Encoder for Smart Meter Data Compression |
| title_full_unstemmed | Frequency Selective Auto-Encoder for Smart Meter Data Compression |
| title_short | Frequency Selective Auto-Encoder for Smart Meter Data Compression |
| title_sort | frequency selective auto encoder for smart meter data compression |
| topic | data compression smart meter auto-encoder digital signal processing |
| url | https://www.mdpi.com/1424-8220/21/4/1521 |
| work_keys_str_mv | AT jihoonlee frequencyselectiveautoencoderforsmartmeterdatacompression AT seungwookyoon frequencyselectiveautoencoderforsmartmeterdatacompression AT euiseokhwang frequencyselectiveautoencoderforsmartmeterdatacompression |