Comparison of lossless compression schemes for high rate electrical grid time series for smart grid monitoring and analysis

Jumar, Richard; Maaß, H.; Hagenmeyer, Veit

doi:10.1016/j.compeleceng.2018.07.008

Cited by 24 publications

(14 citation statements)

References 7 publications

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“…Depending on the data acquisition method, they have been obtained as audio formats or logged in text files. The size of the logged data is almost linearly proportional to the sampling frequency (Kelly and Knottenbelt, 2015).…”

Section: B Electrical Energy Data Acquisition Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Compression Techniques of Electrical Energy Data for Load Monitoring: A Review

Dahunsi¹,

Somefun²,

Ponnle³

et al. 2021

Nig. J. Technol. Dev.

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In recent years, the electric grid has experienced increasing deployment, use, and integration of smart meters and energy monitors. These devices transmit big time-series load data representing consumed electrical energy for load monitoring. However, load monitoring presents reactive issues concerning efficient processing, transmission, and storage. To promote improved efficiency and sustainability of the smart grid, one approach to manage this challenge is applying data-compression techniques. The subject of compressing electrical energy data (EED) has received quite an active interest in the past decade to date. However, a quick grasp of the range of appropriate compression techniques remains somewhat a bottleneck to researchers and developers starting in this domain. In this context, this paper reviews the compression techniques and methods (lossy and lossless) adopted for load monitoring. Selected top-performing compression techniques metrics were discussed, such as compression efficiency, low reconstruction error, and encoding-decoding speed. Additionally reviewed is the relation between electrical energy, data, and sound compression. This review will motivate further interest in developing standard codecs for the compression of electrical energy data that matches that of other domains.

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Section: B Electrical Energy Data Acquisition Systemsmentioning

confidence: 99%

“…Statistical-based Coding (Entropy methods) RLDC, Audio-based codecs, Relatively low (Abuadbba, et al, 2018;Haq, 2018;Sayood, 2018;Firmansah and Setiawan, 2016;Unterweger and Engel, 2016;Kelly and Knottenbelt, 2015) Dictionary-based Coding…”

Section: Losslessmentioning

confidence: 99%

Compression Techniques of Electrical Energy Data for Load Monitoring: A Review

Dahunsi¹,

Somefun²,

Ponnle³

et al. 2021

Nig. J. Technol. Dev.

View full text Add to dashboard Cite

show abstract

“…Full potential access of the frequencies from different stakeholders evolves in energy value chain to avoid the blackouts. Lossless compression of electricity waveform with the sampling rates of kilohertz in range is done with the TTA 2.3.and MP4 ALS techniques (Jumar, Maaß, & Hagenmeyer, 2018).…”

Section: Information and Communication Technologymentioning

confidence: 99%

Integration of 5G Technologies in Smart Grid Communication-A Short Survey

Chandrasekaran

Gunamony

Pratap

2019

Int. J. Renew. Energy Dev.

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Smart grid is an intelligent power distribution system that employs dual communication between the energy devices and the substation. Dual communication helps to overseer the internet access points, energy meters, and power demand of the entire grid. Deployment of advanced communication and control technologies makes smart grid system efficient for energy availability and low-cost maintenance. Appropriate algorithms are analyzed first for the convenient grid to have proper routing and security with a high-level of power transmission and distribution. Information and Communication Technology plays a significant role in monitoring, demand response, and control of the energy distribution. This paper presents a broad review of communication and network technologies with regard to Internet of Things, Machine to Machine Communication, and Cognitive radio terminologies which comprises 5G technology. Networks suitable for future smart-grid are compared with respect to standard protocols, data rate, throughput, delay, security, and routing. Approaches adopted for the smart-grid system has been commended based on the performance and the parameters observed. ©2019. CBIORE-IJRED. All rights reserved

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“…Lossless compression is used for high-resolution or nonpersistent data with a nonvisible pattern. Representative lossless compression methods include entropy coding, dictionary coding, and deflate coding [ 17 ]. In [ 18 ], a lossless compression method that compresses the waveform approximated with few using the Gaussian approximation is proposed.…”

Section: Introductionmentioning

confidence: 99%

Frequency Selective Auto-Encoder for Smart Meter Data Compression

Lee

Yoon

Hwang

2021

Sensors

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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.

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Comparison of lossless compression schemes for high rate electrical grid time series for smart grid monitoring and analysis

Cited by 24 publications

References 7 publications

Compression Techniques of Electrical Energy Data for Load Monitoring: A Review

Compression Techniques of Electrical Energy Data for Load Monitoring: A Review

Integration of 5G Technologies in Smart Grid Communication-A Short Survey

Frequency Selective Auto-Encoder for Smart Meter Data Compression

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