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

Authors

  • F. M. Dahunsi The Federal University of Technology, Akure
  • O. A. Somefun Computer Engineering Department, The Federal University of Technology, Akure, Ondo State, Nigeria
  • A. A. Ponnle
  • K. B. Adedeji

Keywords:

Compression, Smart meter, Load monitoring, Energy data, Load management

Abstract

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.

References

Abuadbba, A.; I. Khalil and X. Yu (2018). Gaussian Approximation-Based Lossless Compression of Smart Meter Readings. IEEE Transactions on Smart Grid, 9: 5047 - 5056. doi:10.1109/TSG.2017.2679111.

Atif, S. M.; S. Qazi and N. Gillis (2019). Improved SVD-Based Initialization for Nonnegative Matrix Factorization Using Low-Rank Correction. Pattern Recognition Letters, 122:53-59. doi:10.1016/j.patrec.2019.02.018.

Basu, K. (2015). Classification Techniques for Non-Intrusive Load Monitoring and Prediction of Residential Loads. Ph.D. Dissertation Université de Grenoble, France.

Batra, N.; H. Dutta and A. Singh (2013). Indic: Improved Non-Intrusive Load Monitoring Using Load Division and Calibration. 2013 12th International Conference on Machine Learning and Applications, Washington DC, USA. 1, 79 – 84, USA: IEEE.

Bellasi, D.; M. Crescentini; D. Cristaudo; A. Romani; M. Tartagni and L. Benini (2019). A Broadband Multi-Mode Compressive Sensing Current Sensor SoC in 0.16 m CMOS. IEEE Transactions on Circuits and Systems I: Regular Papers, 66: 105 - 118.

doi:10.1109/TCSI.2018.2846573.

Chandak, S.; K. Tatwawadi; C. Wen; L. Wang; J. Aparicio and T. Weissman (2020). LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction. 2020 Data Compression Conf. (DCC), 342–351. UT, USA. https://doi.org/10.1109/DCC47342.2020.00042.

Clark, M. and Lampe, L. (2015). Single-Channel Compressive Sampling of Electrical Data for Non-Intrusive Load Monitoring. 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, Florida, 790 – 794, USA: IEEE. doi:10.1109/GlobalSIP.2015.7418305.

Dukish, B. (2009). Extreme Fundamentals of Technology: A Primer of Computers, Electronics, and Technology. Fixtron Corporation.

Fagiani, M.; R. Bonfigli; E. Principi; S. Squartini and L. Mandolini (2019). A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks. Energies, 12: 1371. doi:10.3390/en12071371.

Firmansah, L. and Setiawan, E. B. (2016). Data Audio Compression Lossless FLAC Format to Lossy Audio MP3 Format with Huffman Shift Coding Algorithm. 2016 4th International Conference on Information and Communication Technology (ICoICT), pp. 1 - 5.

doi:10.1109/ICoICT.2016.7571951.

Gerek, Ö. N. and Ece, D. G. (2008). Compression of Power Quality Event Data Using 2D Representation. Electric Power Systems Research, 78: 1047 - 1052. doi:10.1016/j.epsr.2007.08.006.

Gray, R. M. (2011). Entropy and Information Theory (Second ed.). Springer US. doi:10.1007/978-1-4419-7970-4

Hans, M. and Schafer, R. W. (2001). Lossless Compression of Digital Audio. IEEE Signal Processing Magazine, 18: 21 - 32. doi:10.1109/79.939834.

Haq, A. U. (2018). Appliance Event Detection for Non-Intrusive Load Monitoring in Complex Environments. PhD Dissertation, Technische Universität München, München, Germany.

Haq, A. U. and Jacobsen, H. A. (2018). Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review. Energies, 11 (1): 189, 1-22. doi:10.3390/en11010189.

Huang, X.; T. Hu; C. Ye; G. Xu; X. Wang and L. Chen (2019). Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders. Energies, 12: 653. doi:10.3390/en12040653.

Jumar, R.; H. Maaß and V. Hagenmeyer (2018). Comparison of Lossless Compression Schemes for High-Rate Electrical Grid Time Series for Smart Grid Monitoring and Analysis. Computers and Electrical Engineering, 71: 465 - 476. doi:10.1016/j.compeleceng.2018.07.008.

Kelly, J. and Knottenbelt, W. (2015). The UK-DALE Dataset, Domestic Appliance-Level Electricity Demand and Whole-House Demand from Five UK Homes. Scientific Data, 2(150007): 1-14. doi:10.1038/sdata.2015.7

Le, X.-C. (2017). Improving Performance of Non-Intrusive Load Monitoring with Low-Cost Sensor Networks. Ph.D. dissertation.

Lendák, I. and Horvath T. (2019). Efficient Load Profiling and Forecasting in Large Electric Power Systems, Paper presented at 19th Conference on Information Technologies – Applications and Theory (ITAT 2019), Donovaly, Slovakia, 36 – 43, Slovakia: CUER-WS.

Maher, R. C. (2003). Lossless Audio Coding. In Lossless Compression Handbook. (1st Edition. ed.). Elsevier.

Muin, F. A.; T. S. Gunawan; M. Kartiwi and Elsheikh, E. M. (2017). A Review of Lossless Audio Compression Standards and Algorithms. American Institute of Physics Conference Proceedings, 1883, 020006, Bydgoszcz, Poland, 1-11, USA: AIP. doi:10.1063/1.5002024.

Nithiyananthan, K. and Ramachandran, V. (2014). Effective Data Compression Model for On-Line Power System Applications. International Journal of Electrical Energy, Engineering Modelling 27 (3-4): 101-109. doi:10.12720/ijoee.2.2.138-145.

Pu, I. M. (2006). Chapter 9 - Audio Compression. In I. M. Pu (Ed.), Fundamental Data Compression. 171 - 188. Oxford: Butterworth-Heinemann. doi:10.1016/B978-075066310-6/50012-X.

Ringwelski, M.; C. Renner; A. Reinhardt; A. Weigel and V. Turau (2012). The Hitchhiker's Guide to Choosing the Compression Algorithm for Your Smart Meter Data. 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), Florence, Italy, 935 – 940, USA: IEEE.. doi:10.1109/EnergyCon.2012.6348285.

Rodriguez-Silva, A. and Makonin, S. (2019). Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps, 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC2019), Macao, China, 1-6, USA: IEEE.. Arxiv. arXiv:1907.06299 [eess].

Sadler, C. M. and Martonosi, M. (2006). Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks. Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, Colorado, USA, pp. 265 - 278, USA: Boulder: Association for Computing Machinery.. doi:10.1145/1182807.1182834.

Salomon, D. (2008). A Concise Introduction to Data Compression (1st Edition. ed.). Springer.

Sari, E. M. (2018). Data Compression for Smart Grid Infrastructure. Unpublished M.Sc Thesis, Department of Electronics Engineering, Isik University, Turkey

Sarkar, S. J.; P. K. Kundu and G. Sarkar (2018). Development of Lossless Compression Algorithms for Power System Operational Data. IET Generation, Transmission and amp; Distribution, 12: 4045 - 4052.

Sayood, K. (2017). Introduction to Data Compression (Fifth ed.). Morgan Kaufmann Series in Multimedia Information and Systems, Amsterdam, Netherlands.

Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois Press, Urbana, Illinois.

Tame, J. (2019). Approaches to Entropy. Springer Publishers, Singapore. doi:10.1007/978-981-13-2315-7.

Tariq, Z. B.; N. Arshad and M. Nabeel (2015). Enhanced LZMA and BZIP2 for Improved Energy Data Compression. 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Lisbon, Portugal, 1 – 8, Portugal, INSTICC.

Tcheou, M. P.; L. Lovisolo; M. V. Ribeiro.; E. E. Da Silva.; M. A. Rodrigues; J. M. Romano and P. S. Diniz (2014). The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends. IEEE Transactions on Smart Grid, 5: 291 - 302. doi:10.1109/TSG.2013.2293957.

Tong, X.; C. Kang, and Q. Xia (2016). Smart Metering Load Data Compression Based on Load Feature Identification. IEEE Transactions on Smart Grid, 7: 2414 - 2422. doi:10.1109/TSG.2016.2544883.

Unterweger, A. and Engel, D. (2015). Resumable Load Data Compression in Smart Grids. IEEE Transactions on Smart Grid, 6: 919 - 929. doi:10.1109/TSG.2014.2364686.

Unterweger, A. and Engel, D. (2016). Lossless Compression of High-Frequency Voltage and Current Data in Smart Grids. 2016 IEEE International Conference on Big Data (Big Data), Washington D.C., USA, 3131 - 3139, USA: IEEE. doi:10.1109/BigData.2016.7840968.

Wang, C. and Zhai, M. (2018). A Non-Intrusive Load Decomposition Method for Residents. IOP Conference Series: Earth and Environmental Science, 199: 052034. doi:10.1088/1755-1315/199/5/052034.

Wang, Y.; Q. Chen and C. Kang (2020). Smart Meter Data Analytics: Electricity Consumer Behavior Modeling, Aggregation, and Forecasting. Springer Publishers, Singapore. doi:10.1007/978-981-15-2624-4.

Wang, Y.; Q. Chen; T. Hong and C. Kang (2019). Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges. IEEE Transactions on Smart Grid, 10: 3125-3148. doi:10.1109/TSG.2018.2818167.

Wang, Y.; Q. Chen; C. Kang; Q. Xia and M. Luo (2017). Sparse and Redundant Representation-Based Smart Meter Data Compression and Pattern Extraction. IEEE Transactions on Power Systems, 32: 2142 - 2151. doi:10.1109/TPWRS.2016.2604389.

Wang, Y.; Q. Chen; C. Kang; Q. Xia; Y. Tan; Z. Zeng and M. Luo (2016). Residential Smart Meter Data Compression and Pattern Extraction via Non-Negative K-SVD. 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, USA, 1 - 5, USA: IEEE. doi:10.1109/PESGM.2016.7741464.

Wang, Y.; Q. Chen; C. Kang; M. Zhang; K. Wang and Y. Zhao (2015). Load Profiling and Its Application to Demand Response: A Review. Tsinghua Science and Technology, 20: 117 - 129. doi:10.1109/TST.2015.7085625.

Wang, Y.; A. Filippi; R. Rietman; and G. Leus (2012). Compressive Sampling for Non-Intrusive Appliance Load Monitoring (NALM) Using Current Waveforms. Signal Processing, Pattern Recognition and Applications / 779:

Computer Graphics and Imaging. Crete: ACTAPRESS. doi:10.2316/P.2012.778-024.

Wee, C. K. and Nayak, R. (2019). An Approach to Compress and Represents Time Series Data and Its Application in Electric Power Utilities. In R. Islam, Y. S. Koh, Y. Zhao, G. Warwick, D. Stirling, C.-T. Li, and Z. Islam (Ed.), Data Mining, pp. 107 - 120. Singapore: Springer. doi:10.1007/978-981-13-6661-1_9.

Wen, L.; K. Zhou.; S. Yang and L. Li (2018). Compression of Smart Meter Big Data: A Survey. Renewable and Sustainable Energy Reviews, 91: 59 - 69.

Wong, Y. F.; A. Y. Sekercioglu; T. Drummond, and V. S. Wong (2013). Recent Approaches to Non-Intrusive Load Monitoring Techniques in Residential Settings. 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG). Singapore, 73 - 79, USA: IEEE.

doi:10.1109/CIASG.2013.6611501.

Yan, L.; J. Han; R. Xu and Z. Li (2019). LIFTED: Household Appliance-Level Load Dataset and Data Compression with Lossless Coding Considering Precision, Virtual 2020 IEEE Power and Energy Society General Meeting (PESGM), 1-5, USA: IEEE. arXiv:1911.01581 [eess].

Zhuang, M.; M. Shahidehpour and Z. Li (2018). An Overview of Non-Intrusive Load Monitoring: Approaches, Business Applications, and Challenges. 2018 International Conference on Power System Technology (POWERCON), Guangzhou, China: 4291 – 4299, USA: IEEE.. doi:10.1109/POWERCON.2018.8601534.

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2021-10-06

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