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On the Applicability of 2D Local Binary Patterns for Identifying Electrical Appliances in Non-intrusive Load Monitoring

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1252))

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Abstract

In recent years, the automatic identification of electrical devices through their power consumption signals finds a variety of applications in smart home monitoring and non-intrusive load monitoring (NILM). This work proposes a novel appliance identification scheme and introduces a new feature extraction method that represents power signals in a 2D space, similar to images and then extracts their properties. In this context, the local binary pattern (LBP) and other variants are investigated on their ability to extract histograms of 2D binary patterns of power signals. Specifically, by moving to a 2D representation space, each power sample is surrounded by eight neighbors at least. This can help extracting pertinent characteristics and providing more possibilities to encode power signals robustly. Moreover, the proposed identification technique has the main advantage of accurately recognizing the electrical devices independently of their states and on/off events, unlike existing models. Three public databases including real household power consumption measurements at the appliance-level are employed to assess the performance of the proposed system while considering various machine learning classifiers. The promising performance obtained in terms of accuracy and F-score proves the successful application of the 2D LBP in recognizing electrical devices and creates new possibilities for energy efficiency based on NILM models.

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References

  1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    MATH  Google Scholar 

  2. Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) Computer Vision - ECCV 2004, pp. 469–481. Springer, Heidelberg (2004)

    Google Scholar 

  3. Ahsan, T., Jabid, T., Chong, U.-P.: Facial expression recognition using local transitional pattern on Gabor filtered facial images. IETE Tech. Rev. 30(1), 47–52 (2013)

    Google Scholar 

  4. Alsalemi, A., Himeur, Y., Bensaali, F., Amira, A., Sardianos, C., Varlamis, I., Dimitrakopoulos, G.: Achieving domestic energy efficiency using micro-moments and intelligent recommendations. IEEE Access 8, 15047–15055 (2020)

    Google Scholar 

  5. Alsalemi, A., Sardianos, C., Bensaali, F., Varlamis, I., Amira, A., Dimitrakopoulos, G.: The role of micro-moments: a survey of habitual behavior change and recommender systems for energy saving. IEEE Syst. J. 13(3), 3376–3387 (2019)

    Google Scholar 

  6. Alsalemi, A., Bensaali, F., Amira, A., Fetais, N., Sardianos, C., Varlamis, I.: Smart energy usage and visualization based on micro-moments. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Intelligent Systems and Applications, pp. 557–566. Springer, Cham (2020)

    Google Scholar 

  7. Alsalemi, A., Ramadan, M., Bensaali, F., Amira, A., Sardianos, C., Varlamis, I., Dimitrakopoulos, G.: Boosting domestic energy efficiency through accurate consumption data collection. In: 5th International Symposium on Real-Time Data Processing for Cloud Computing (RTDPCC), Leicester, UK (2019)

    Google Scholar 

  8. Alsalemi, A., Ramadan, M., Bensaali, F., Amira, A., Sardianos, C., Varlamis, I., Dimitrakopoulos, G.: Endorsing domestic energy saving behavior using micro-moment classification. Appl. Energy 250, 1302–1311 (2019)

    Google Scholar 

  9. Amasyali, K., El-Gohary, N.M.: A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205 (2018)

    Google Scholar 

  10. Guedes, J.D.S., Ferreira, D.D., Barbosa, B.H.G., Duque, C.A., Cerqueira, A.S.: Non-intrusive appliance load identification based on higher-order statistics. IEEE Latin Am. Trans. 13(10), 3343–3349 (2015)

    Google Scholar 

  11. Dinesh, C., Nettasinghe, B.W., Godaliyadda, R.I., Ekanayake, M.P.B., Ekanayake, J., Wijayakulasooriya, J.V.: Residential appliance identification based on spectral information of low frequency smart meter measurements. IEEE Trans. Smart Grid 7(6), 2781–2792 (2016)

    Google Scholar 

  12. Gao, J., Giri, S., Kara, E.C., Bergés, M.: PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, BuildSys 2014, pp. 198–199. ACM, New York (2014)

    Google Scholar 

  13. Ghosh, S., Chatterjee, A., Chatterjee, D.: Improved non-intrusive identification technique of electrical appliances for a smart residential system. IET Gener. Transm. Distrib. 13(5), 695–702 (2019)

    Google Scholar 

  14. Himeur, Y., Elsalemi, A., Bensaali, F., Amira, A.: Efficient multi-descriptor fusion for non-intrusive appliance recognition. In: The IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5, May 2020

    Google Scholar 

  15. Himeur, Y., Elsalemi, A., Bensaali, F., Amira, A.: Improving in-home appliance identification using fuzzy-neighbors-preserving analysis based QR-decomposition. In: International Congress on Information and Communication Technology (ICICT), pp. 1–8, February 2020

    Google Scholar 

  16. Houidi, S., Auger, F., Sethom, H.B.A., Fourer, D., Miègeville, L.: Multivariate event detection methods for non-intrusive load monitoring in smart homes and residential buildings. Energy Build. 208, 109624 (2020)

    Google Scholar 

  17. Kahl, M., Haq, A.U., Kriechbaumer, T., Jacobsen, H.-A.: Whited-a worldwide household and industry transient energy data set. In: 3rd International Workshop on Non-Intrusive Load Monitoring (2016)

    Google Scholar 

  18. Kruti, R., Patil, A., Gornale, S.S.: Fusion of local binary pattern and local phase quantization features set for gender classification using fingerprints. Int. J. Comput. Sci. Eng. 7(1), 22–29 (2019)

    Google Scholar 

  19. Ma, M., Lin, W., Zhang, J., Wang, P., Zhou, Y., Liang, X.: Toward energy-awareness smart building: discover the fingerprint of your electrical appliances. IEEE Trans. Ind. Inf. 14(4), 1458–1468 (2018)

    Google Scholar 

  20. Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., Tonello, A.M.: GREEND: an energy consumption dataset of households in Italy and Austria. In: IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 511–516, November 2014

    Google Scholar 

  21. Morais, L.R., Castro, A.R.G.: Competitive autoassociative neural networks for electrical appliance identification for non-intrusive load monitoring. IEEE Access 7, 111746–111755 (2019)

    Google Scholar 

  22. Park, S.W., Baker, L.B., Franzon, P.D.: Appliance identification algorithm for a non-intrusive home energy monitor using cogent confabulation. IEEE Trans. Smart Grid 10(1), 714–721 (2019)

    Google Scholar 

  23. Srinivasa Perumal, R., Chandra Mouli, P.V.S.S.R.: Dimensionality reduced local directional pattern (DR-LDP) for face recognition. Expert Syst. Appl. 63, 66–73 (2016)

    Google Scholar 

  24. Sardianos, C., Varlamis, I., Chronis, C., Dimitrakopoulos, G., Alsalemi, A., Himeur, Y., Bensaali, F., Amira, A.: A model for predicting room occupancy based on motion sensor data, vol. 45, September 2020

    Google Scholar 

  25. Sardianos, C., Varlamis, I., Dimitrakopoulos, G., Anagnostopoulos, D., Alsalemi, A., Bensaali, F., Amira A.: “i want to... change”: micro-moment based recommendations can change users’ energy habits. In: Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2019), pp. 30–39. SCITEPRESS (2019)

    Google Scholar 

  26. Wang, R., Ji, W., Liu, M., Wang, X., Weng, J., Deng, S., Gao, S., Yuan, C.A.: Review on mining data from multiple data sources. Pattern Recogn. Lett. 109, 120–128 (2018). Special Issue on Pattern Discovery from Multi-Source Data (PDMSD)

    Google Scholar 

  27. Wang, Z., Zheng, G.: Residential appliances identification and monitoring by a nonintrusive method. IEEE Trans. Smart Grid 3(1), 80–92 (2012)

    Google Scholar 

  28. Welikala, S., Dinesh, C., Ekanayake, M.P.B., Godaliyadda, R.I., Ekanayake, J.: Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting. IEEE Trans. Smart Grid 10(1), 448–461 (2019)

    Google Scholar 

  29. Wu, C.-H., Lai, C.-C., Lo, H.-J., Wang, P.-S.: A comparative study on encoding methods of local binary patterns for image segmentation. In: International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, pp. 277–283. Springer (2018)

    Google Scholar 

  30. Xiao, Y., Hu, Y., He, H., Zhou, D., Zhao, Y., Hu, W.: Non-intrusive load identification method based on improved KM algorithm. IEEE Access 7, 151368–151377 (2019)

    Google Scholar 

  31. Yan, D., Jin, Y., Sun, H., Dong, B., Ye, Z., Li, Z., Yuan, Y.: Household appliance recognition through a Bayes classification model. Sustain. Cities Soc. 46, 101393 (2019)

    Google Scholar 

  32. Yuan, J.-H., Zhu, H.-D., Gan, Y., Shang, L.: Enhanced local ternary pattern for texture classification. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) Intelligent Computing Theory, pp. 443–448. Springer, Cham (2014)

    Google Scholar 

  33. Zeifman, M., Roth, K.: Nonintrusive appliance load monitoring: review and outlook. IEEE Trans. Consum. Electron. 57(1), 76–84 (2011)

    Google Scholar 

  34. Zhiren, R., Bo, T., Longfeng, W., Hui, L., Yanfei, L., Haiping, W.: Non-intrusive load identification method based on integrated intelligence strategy. In: 2019 25th International Conference on Automation and Computing (ICAC), pp. 1–6, September 2019

    Google Scholar 

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Acknowledgments

This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Yassine Himeur, Abdullah Alsalemi & Faycal Bensaali

  2. Institute of Artificial Intelligence, De Montfort University, Leicester, UK

    Abbes Amira

  3. Department of Informatics and Telematics, Harokopio University of Athens, Kallithea, Greece

    Christos Sardianos, Iraklis Varlamis & George Dimitrakopoulos

Authors
  1. Yassine Himeur
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  2. Abdullah Alsalemi
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  3. Faycal Bensaali
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  4. Abbes Amira
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  5. Christos Sardianos
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  6. Iraklis Varlamis
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  7. George Dimitrakopoulos
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Corresponding author

Correspondence to Yassine Himeur .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Himeur, Y. et al. (2021). On the Applicability of 2D Local Binary Patterns for Identifying Electrical Appliances in Non-intrusive Load Monitoring. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_15

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