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A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

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Abstract

The aim of non-intrusive appliance load monitoring (NIALM) is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to ON-OFF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness-of-fit (GOF) methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-OFF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K-means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K-means clustering. The results of the algorithm implementation were discussed and ideas on future work were also proposed.

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References

  1. Hart G W. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 1992, 80(12): 1870–1891

    Article  Google Scholar 

  2. Yang C C, Soh C S, Yap V V. Comparative study of event detection methods for non-intrusive appliance load monitoring. Energy Procedia, 2014, 61, 1840–1843

    Article  Google Scholar 

  3. Zeifman M, Roth K. Nonintrusive appliance load monitoring: review and outlook. IEEE Transactions on Consumer Electronics, 2011, 57(1): 76–84

    Article  Google Scholar 

  4. Zoha A, Gluhak A, Imran M A, Rajasegarar S. Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors (Basel), 2012, 12(12): 16838–16866

    Article  Google Scholar 

  5. Wong Y F, Sekercioglu Y A, Drummond T, Wong V S. Recent approaches to non-intrusive load monitoring techniques in residential settings. In: IEEE Symposium on Computational Intelligence Applications in Smart Grid. Singapore, 2013, 73–79

    Google Scholar 

  6. Norford L K, Leeb S B. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Building, 1996, 24(1): 51–64

    Article  Google Scholar 

  7. Laughman C, Lee K, Cox R, Shaw S, Leeb S, Norford L, Armstrong P. Power signature analysis. IEEE Power and Energy Magazine, 2003, 1(2): 56–63

    Article  Google Scholar 

  8. Liang J, Ng S K K, Kendall G, Cheng JWM. Ng S K K, Kendall G, Cheng J W M. Load signature study Part I: basic concept, structure, and methodology. IEEE Transactions on Power Delivery, 2010, 25(2): 551–560

    Article  Google Scholar 

  9. Lee W K, Fung G S K, Lam H Y, Chan F H Y, Lucente M. Exploration on load signatures. In Proceedings of International Conference on Electrical Engineering (ICEE), Sapporo, Japan, 2004, 1–5

    Google Scholar 

  10. Lam H Y, Fung G S K, Lee W K. A novel method to construct taxonomy electrical appliances based on load signatures. IEEE Transactions on Consumer Electronics, 2007, 53(2): 653–660

    Article  Google Scholar 

  11. Patel S N, Robertson T, Kientz J A, Reynolds M S, Abowd G D. At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In: Proceedings of the 9th International Conference on Ubiquitous Computing. Innsbruck, Austria, 2007, 271–288

    Google Scholar 

  12. Gupta S, Reynolds M S, Patel S N. ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing. Copenhagen, Denmark, 2010, 139–148

    Google Scholar 

  13. Leeb S B, Shaw S R, Kirtley J L. Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Transactions on Power Delivery, 1995, 10(3): 1200–1210

    Article  Google Scholar 

  14. Chang H H, Yang H T, Lin C L. Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. Lecture Notes in Computer Science, 2008, 5236: 664–674

    Google Scholar 

  15. Figueiredo MB, Almeida A D, Ribeiro B. An experimental study on electrical signature identification of non-intrusive load monitoring (NILM) systems. Lecture Notes in Computer Science, 2011, 6594: 31–40

    Google Scholar 

  16. Chang H H. Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies, 2012, 5(12): 4569–4589

    Article  Google Scholar 

  17. Shaw S R, Leeb S B, Norford L K, Cox R W. Nonintrusive load monitoring and diagnostics in power systems. IEEE Transactions on Instrumentation and Measurement, 2008, 57(7): 1445–1454

    Article  Google Scholar 

  18. Hazas M, Friday A, Scott J. Look back before leaping forward: four decades of domestic energy inquiry. IEEE Pervasive Computing, 2011, 10(1): 13–19

    Article  Google Scholar 

  19. Anderson K, Berges M, Ocneanu A, Benitez D, Moura J MF. Event detection for non-intrusive load monitoring. In: Proceedings of the 38th Annual Conference on IEEE Industrial Electronics Society (IECON). Montreal, Canada, 2012, 3312–3317

    Google Scholar 

  20. Zico Kolter J, Johnson M J. REDD: a public data set for energy disaggregation research. In: Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability. San Diego, USA, 2011, 1–6

    Google Scholar 

  21. Arias-Castro E, Donoho D L. Does median filtering truly preserve edges better than linear filtering? Annals of Statistics, 2009, 37(3): 1172–1206

    Article  MATH  MathSciNet  Google Scholar 

  22. Giri S, Lai P, Berges M. Novel techniques for ON and OFF states detection of appliances for power estimation in non-intrusive load monitoring. In: Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC). Montreal, Canada, 2013, 522–530

    Google Scholar 

  23. Jin Y, Tebekaemi E, Berges M, Soibelman L. Robust adaptive event detection in non-intrusive load monitoring for energy aware smart facilities. In: Proceedings of the 2011 International Conference on Acoustics, Speech and Signal Processing (ICASSP’11). Prague, Czech Republic, 2011, 4340–4343

    Google Scholar 

  24. U.S. Department of Energy. Estimating Appliances and Home Electronic Energy Use. 2014-11-05, http://energy.gov/energysaver/articles/estimating-appliance-and-home-electronic-energy-use

    Google Scholar 

  25. Goncalves H, Ocneanu A, Berges M. Unsupervised disaggregation of appliances using aggregated consumption data. In: Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability, San Diego, USA, 2011, 1–6

    Google Scholar 

  26. Wang Z, Zheng G. Residential appliances identification and monitoring by a nonintrusive method. IEEE Transactions on Smart Grid, 2012, 3: 80–92

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering and Green Technology, University Tunku Abdul Rahman, Jalan University, Bandar Barat, Kampar, 31900, Perak, Malaysia

    Chuan Choong Yang, Chit Siang Soh & Vooi Voon Yap

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  1. Chuan Choong Yang
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  2. Chit Siang Soh
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  3. Vooi Voon Yap
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Correspondence to Chuan Choong Yang.

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Yang, C.C., Soh, C.S. & Yap, V.V. A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring. Front. Energy 9, 231–237 (2015). https://doi.org/10.1007/s11708-015-0358-6

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  • DOI: https://doi.org/10.1007/s11708-015-0358-6

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