DOI: https://doi.org/10.20535/2523-4455.2018.23.4.143310

Виявлення аномальної поведінки людини у MicroGrid на базі машинного навчання

Oleksandr Mykolajovych Komarevych, Yuriy Vitaliyovych Khokhlov, Yuliia Serhiivna Yamnenko

Анотація


У застосуванні до системи розподіленої генерації MicroGrid розглянуто задачу виявлення аномалій поведінки користувача. Задача вирішується із залученням методів машинного навчання (Machine Learning), зокрема, методу детектування аномалій (Anomaly Detection). В якості ключових параметрів для найпростішого випадку задачі виявлення аномальної поведінки розглянуто усереднене електроспоживання за п’ятихвилинні проміжки часу, а також кількість спрацювань датчика руху, встановленого у приміщенні MicroGrid.

Бібл. 12, рис. 4, табл. 2.


Ключові слова


MicroGrid; Anomaly Detection; Machine Learning

Повний текст:

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Посилання


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D. Q. Oliveira, A. C. Zambroni de Souza, A. B. Almeida, M. V. Santos, B. I. L. Lopes, and D. Marujo, “Microgrid management in emergency scenarios for smart electrical energy usage,” in 2015 IEEE Eindhoven PowerTech, 2015, pp. 1–6, DOI: 10.1109/PTC.2015.7232309.

J. Yamnenko, T. Tereshchenko, L. Klepach, and D. Palii, “Forecasting of electricity consumption in SmartGrid,” in 2017 International Conference on Modern Electrical and Energy Systems (MEES), 2017, pp. 208–211, DOI: 10.1109/MEES.2017.8248891.

S. Singh and N. Singh, “Big Data analytics,” in 2012 International Conference on Communication, Information & Computing Technology (ICCICT), 2012, pp. 1–4, DOI: 10.1109/ICCICT.2012.6398180.

A. Gulenko, M. Wallschlager, F. Schmidt, O. Kao, and F. Liu, “Evaluating machine learning algorithms for anomaly detection in clouds,” in 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 2716–2721, DOI: 10.1109/BigData.2016.7840917.

Xueqin Zhang, Chunhua Gu, and Jiajun Lin, “Support Vector Machines for Anomaly Detection,” in 2006 6th World Congress on Intelligent Control and Automation, 2006, pp. 2594–2598, DOI: 10.1109/WCICA.2006.1712831.

Y. S. Yamnenko and T. O. Tereshchenko, “Spectral methods for processing biotelemetrical data,” Electron. Commun., vol. 21, no. 4, pp. 38–43, Nov. 2016, DOI: 10.20535/2312-1807.2016.21.4.81904.

X. Song, M. Wu, C. Jermaine, and S. Ranka, “Conditional Anomaly Detection,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 5, pp. 631–645, 2007, DOI: 10.1109/TKDE.2007.1009.

H. M. Abdullah and A. M. Zeki, “Frontend and Backend Web Technologies in Social Networking Sites: Facebook as an Example,” in 2014 3rd International Conference on Advanced Computer Science Applications and Technologies, 2014, pp. 85–89, DOI: 10.1109/ACSAT.2014.22.

M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “LOF,” in Proceedings of the 2000 ACM SIGMOD international conference on Management of data - SIGMOD ’00, 2000, pp. 93–104, DOI: 10.1145/342009.335388.

P. S. Badase, G. P. Deshbhratar, and A. P. Bhagat, “Classification and analysis of clustering algorithms for large datasets,” in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, pp. 1–5, DOI: 10.1109/ICIIECS.2015.7193191.

R. Lin, E. Khalastchi, and G. A. Kaminka, “Detecting anomalies in unmanned vehicles using the Mahalanobis distance,” in 2010 IEEE International Conference on Robotics and Automation, 2010, pp. 3038–3044, DOI: 10.1109/ROBOT.2010.5509781.


Перелік посилань за IEEE 2006


  1. T. M. Baziuk, I. V. Blinovv, O. F. Butkevich, I. S. Honcharenko, S. P. Denysiuk, and et. al., Intelektualʹni elektrychni merezhi: elementy ta rezhymy [Intelligent Electric Networks: Elements and Modes]. Kyiv, Ukraine: The National Academy of Sciences of Ukraine The Institute of Electrodynamics, 2016, ISBN: 978-966-02-7913-1.
  2. D. Q. Oliveira, A. C. Zambroni de Souza, A. B. Almeida, M. V. Santos, B. I. L. Lopes, and D. Marujo, “Microgrid management in emergency scenarios for smart electrical energy usage,” in 2015 IEEE Eindhoven PowerTech, 2015, pp. 1–6, DOI: 10.1109/PTC.2015.7232309.
  3. J. Yamnenko, T. Tereshchenko, L. Klepach, and D. Palii, “Forecasting of electricity consumption in SmartGrid,” in 2017 International Conference on Modern Electrical and Energy Systems (MEES), 2017, pp. 208–211, DOI: 10.1109/MEES.2017.8248891.
  4. S. Singh and N. Singh, “Big Data analytics,” in 2012 International Conference on Communication, Information & Computing Technology (ICCICT), 2012, pp. 1–4, DOI: 10.1109/ICCICT.2012.6398180.
  5. A. Gulenko, M. Wallschlager, F. Schmidt, O. Kao, and F. Liu, “Evaluating machine learning algorithms for anomaly detection in clouds,” in 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 2716–2721, DOI: 10.1109/BigData.2016.7840917.
  6. Xueqin Zhang, Chunhua Gu, and Jiajun Lin, “Support Vector Machines for Anomaly Detection,” in 2006 6th World Congress on Intelligent Control and Automation, 2006, pp. 2594–2598, DOI: 10.1109/WCICA.2006.1712831.
  7. Y. S. Yamnenko and T. O. Tereshchenko, “Spectral methods for processing biotelemetrical data,” Electron. Commun., vol. 21, no. 4, pp. 38–43, Nov. 2016, DOI: 10.20535/2312-1807.2016.21.4.81904.
  8. X. Song, M. Wu, C. Jermaine, and S. Ranka, “Conditional Anomaly Detection,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 5, pp. 631–645, 2007, DOI: 10.1109/TKDE.2007.1009.
  9. H. M. Abdullah and A. M. Zeki, “Frontend and Backend Web Technologies in Social Networking Sites: Facebook as an Example,” in 2014 3rd International Conference on Advanced Computer Science Applications and Technologies, 2014, pp. 85–89, DOI: 10.1109/ACSAT.2014.22.
  10. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “LOF,” in Proceedings of the 2000 ACM SIGMOD international conference on Management of data - SIGMOD ’00, 2000, pp. 93–104, DOI: 10.1145/342009.335388.
  11. P. S. Badase, G. P. Deshbhratar, and A. P. Bhagat, “Classification and analysis of clustering algorithms for large datasets,” in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, pp. 1–5, DOI: 10.1109/ICIIECS.2015.7193191.
  12. R. Lin, E. Khalastchi, and G. A. Kaminka, “Detecting anomalies in unmanned vehicles using the Mahalanobis distance,” in 2010 IEEE International Conference on Robotics and Automation, 2010, pp. 3038–3044, DOI: 10.1109/ROBOT.2010.5509781.


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Copyright (c) 2018 Комаревич О. М., Хохлов Ю. В., Ямненко Ю. С.

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ISSN: 2523-4447
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