A future extension to this research is to investigate the proposed approach while considering extended categories of appliances. The proposed system performance depends on the chosen system parameters like resolution, reference segment length, quantization scheme, parameter extraction, and classification algorithms. Development of an automatic mechanism to choose the optimal system parameters for a targeted application is another future task. Another research axis is investigating the system performance for higher resolution, adaptive quantization schemes and other robust classifiers like Rotation Forest, Random Forest, etc.
REFERENCES
[1] S. Darby, “Smart metering: what potential for householder engagement?,” Build. Res.
Inf., vol. 38, no. 5, pp. 442–457, 2010.
[2] Q. Sun et al., “A comprehensive review of smart energy meters in intelligent energy networks,” IEEE Internet Things J., vol. 3, no. 4, pp. 464–479, 2015.
[3] D. Alahakoon and X. Yu, “Smart electricity meter data intelligence for future energy systems: A survey,” IEEE Trans. Ind. Inform., vol. 12, no. 1, pp. 425–436, 2015.
[4] Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of smart meter data analytics:
Applications, methodologies, and challenges,” IEEE Trans. Smart Grid, vol. 10, no.
3, pp. 3125–3148, 2018.
[5] G. Barnicoat and M. Danson, “The ageing population and smart metering: A field study of householders’ attitudes and behaviours towards energy use in Scotland,”
Energy Res. Soc. Sci., vol. 9, pp. 107–115, 2015.
[6] N. Uribe-Pérez, L. Hernández, D. De la Vega, and I. Angulo, “State of the art and trends review of smart metering in electricity grids,” Appl. Sci., vol. 6, no. 3, p. 68, 2016.
[7] M. Verhelst and A. Bahai, “Where Analog Meets Digital: Analog? to? Information Conversion and Beyond,” IEEE Solid-State Circuits Mag., vol. 7, no. 3, pp. 67–80, 2015.
[8] J. Laska et al., “Random Sampling for Analog-to-Information Conversion of Wideband Signals,” in 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software, Univ. of Texas at Dallas, Richardson, TX, USA, Oct. 2006, pp. 119–122, doi: 10.1109/DCAS.2006.321048.
[9] “Saudi Vision 2030.” https://vision2030.gov.sa/en (accessed Dec. 06, 2019).
[10] I. Abubakar, S. N. Khalid, M. W. Mustafa, H. Shareef, and M. Mustapha,
“Application of load monitoring in appliances’ energy management – A review,”
Renew. Sustain. Energy Rev., vol. 67, pp. 235–245, Jan. 2017, doi:
10.1016/j.rser.2016.09.064.
[11] W. Souza, F. Garcia, F. Marafao, L. Silva, and M. Simoes, “Load Disaggregation Using Microscopic Power Features and Pattern Recognition,” Energies, vol. 12, p.
2641, Jul. 2019, doi: 10.3390/en12142641.
[12] X. Liu, L. Golab, W. Golab, I. F. Ilyas, and S. Jin, “Smart meter data analytics:
systems, algorithms, and benchmarking,” ACM Trans. Database Syst. TODS, vol. 42, no. 1, pp. 1–39, 2016.
[13] N. Mogles et al., “How smart do smart meters need to be?,” Build. Environ., vol. 125, pp. 439–450, Nov. 2017, doi: 10.1016/j.buildenv.2017.09.008.
[14] D. Alahakoon and X. Yu, “Smart electricity meter data intelligence for future energy systems: A survey,” IEEE Trans. Ind. Inform., vol. 12, no. 1, pp. 425–436, 2015.
[15] P. Koponen et al., Definition of Smart Metering and Applications and Identification of Benefits. 2008.
[16] J. Ponoćko and J. V. Milanović, “Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5446–5455, Sep. 2018, doi: 10.1109/TPWRS.2018.2799903.
[17] Z. Jiang, R. Lin, and F. Yang, “A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data,” Energ. Basel, vol. 11, no. 9, p.
2235, 2018, doi: http://dx.doi.org.effatuniversity.idm.oclc.org/10.3390/en11092235.
[18] X. Chen et al., “Research on data collection key technology of smart electric energy meters,” IOP Conf. Ser. Earth Environ. Sci., vol. 113, p. 012191, Feb. 2018, doi:
10.1088/1755-1315/113/1/012191.
[19] Hongjian Gao, G. Bumiller, and Weilin Liu, “Robust timing synchronization against narrowband interference for PLC systems,” in 18th IEEE International Symposium on Power Line Communications and Its Applications, Mar. 2014, pp. 328–333, doi:
10.1109/ISPLC.2014.6812319.
[20] Z. Xu, M. Zhai, and Y. Zhao, “Optimal Resource Allocation Based on Resource Factor for Power-Line Communication Systems,” Power Deliv. IEEE Trans. On, vol.
25, pp. 657–666, May 2010, doi: 10.1109/TPWRD.2009.2035364.
[21] M. M. C. Ndlovu, “A permutation coding and OFDM-MFSK modulation scheme for power-line communication,” Thesis, 2015.
[22] I. Rouf, H. Mustafa, M. Xu, W. Xu, R. Miller, and M. Gruteser, “Neighborhood watch: security and privacy analysis of automatic meter reading systems,” in Proceedings of the 2012 ACM conference on Computer and communications security - CCS ’12, Raleigh, North Carolina, USA, 2012, p. 462, doi:
10.1145/2382196.2382246.
[23] “The IRDA Collector of Electric Meter Based on Zigbee--《Instrumentation Technology 》 2012 年 12 期 .” http://en.cnki.com.cn/Article_en/CJFDTotal- YBJI201212007.htm (accessed Feb. 03, 2020).
[24] V. Mondini, A. L. Mangia, and A. Cappello, “EEG-based BCI system using adaptive features extraction and classification procedures,” Comput. Intell. Neurosci., vol.
2016, 2016.
[25] K.-S. Fu, Applications of pattern recognition. CRC press, 2019.
[26] S. Biansoongnern and B. Plungklang, “Non-intrusive appliances load monitoring (nilm) for energy conservation in household with low sampling rate,” Procedia Comput. Sci., vol. 86, pp. 172–175, 2016.
[27] A. Reinhardt et al., “On the accuracy of appliance identification based on distributed load metering data,” in 2012 Sustainable Internet and ICT for Sustainability (SustainIT), 2012, pp. 1–9.
[28] M. Weiss, A. Helfenstein, F. Mattern, and T. Staake, “Leveraging smart meter data to recognize home appliances,” in 2012 IEEE International Conference on Pervasive Computing and Communications, 2012, pp. 190–197.
[29] Pattern Recognition and Signal Analysis in Medical Imaging. Elsevier, 2014.
[30] “Pattern Recognition | Introduction,” GeeksforGeeks, May 03, 2018.
https://www.geeksforgeeks.org/pattern-recognition-introduction/ (accessed Dec. 05, 2019).
[31] L. Kuncheva and C. Whitaker, “Pattern Recognition and Classification,” 2015, pp. 1–
7.
[32] P. Sharma and M. Kaur, “Classification in Pattern Recognition: A Review,” 2013.
[33] C. Belley, S. Gaboury, B. Bouchard, and A. Bouzouane, “An efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances,” Pervasive Mob. Comput., vol. 12, pp. 58–78, 2014.
[34] L. Farinaccio and R. Zmeureanu, “Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses,”
Energy Build., vol. 30, no. 3, pp. 245–259, 1999.
[35] N. F. Esa, M. P. Abdullah, and M. Y. Hassan, “A review disaggregation method in Non-intrusive Appliance Load Monitoring,” Renew. Sustain. Energy Rev., vol. 66, pp. 163–173, 2016.
[36] A. Ridi and J. Hennebert, “Hidden Markov Models for ILM appliance identification,”
Procedia Comput. Sci., vol. 32, pp. 1010–1015, 2014.
[37] W. Zucchini, I. L. MacDonald, and R. Langrock, Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. CRC Press, 2017.
[38] A. Ridi, C. Gisler, and J. Hennebert, “ACS-F2—A new database of appliance consumption signatures,” in 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2014, pp. 145–150.
[39] S. Kiran, H. A. Khattak, H. I. Butt, and A. Ahmed, “Towards Efficient Energy Monitoring Using IoT,” in 2018 IEEE 21st International Multi-Topic Conference (INMIC), 2018, pp. 1–4.
[40] “Signal Reconstruction.”
http://pilot.cnxproject.org/content/collection/col10064/latest/module/m10788/latest (accessed Dec. 05, 2019).
[41] “Physically Based Rendering - 3rd Edition.”
https://www.elsevier.com/books/physically-based-rendering/pharr/978-0-12- 800645-0 (accessed Dec. 05, 2019).
[42] V. K. Ingle and J. G. Proakis, Digital signal processing using matlab: a problem solving companion. Cengage Learning, 2016.
[43] S. M. Qaisar, “A Computationally Efficient EEG Signals Segmentation and De- noising Based on an Adaptive Rate Acquisition and Processing,” in 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), 2018, pp. 182–
186.
[44] S. M. Qaisar, L. Fesquet, and M. Renaudin, “Adaptive rate filtering a computationally efficient signal processing approach,” Signal Process., vol. 94, pp. 620–630, 2014.
[45] S. M. Qaisar, D. Dallet, S. Benjamin, P. Desprez, and R. Yahiaoui, “Power efficient analog to digital conversion for the Li-ion battery voltage monitoring and measurement,” in 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2013, pp. 1522–1525.
[46] S. M. Qaisar, R. Yahiaoui, and D. Dominique, “A smart power management system monitoring and measurement approach based on a signal driven data acquisition,” in 2015 Saudi Arabia Smart Grid (SASG), 2015, pp. 1–4.
[47] Y. Hou et al., “A 61-nW Level-Crossing ADC With Adaptive Sampling for Biomedical Applications,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 66, no.
1, pp. 56–60, 2018.
[48] B. A. Moser, “Similarity recovery from threshold-based sampling under general conditions,” IEEE Trans. Signal Process., vol. 65, no. 17, pp. 4645–4654, 2017.
[49] M. Paluszek and S. Thomas, MATLAB machine learning. Apress, 2016.
[50] H. Blockeel and J. Struyf, “Efficient Algorithms for Decision Tree Cross-validation,”
p. 30.
[51] D. Berrar, “Cross-Validation,” in Encyclopedia of Bioinformatics and Computational Biology, S. Ranganathan, M. Gribskov, K. Nakai, and C. Schönbach, Eds. Oxford:
Academic Press, 2019, pp. 542–545.
[52] K. C. Klauer, “Model Testing and Selection, Theory of,” in International Encyclopedia of the Social & Behavioral Sciences, N. J. Smelser and P. B. Baltes, Eds. Oxford: Pergamon, 2001, pp. 9927–9931.
[53] J. D. Rodriguez, A. Perez, and J. A. Lozano, “Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 3, pp. 569–575, Mar. 2010, doi: 10.1109/TPAMI.2009.187.
[54] S. Qaisar and F. Alsharif, “An Adaptive Rate Time-Domain Approach for a Proficient and Automatic Household Appliances Identification,” Jan. 2020, pp. 1–4, doi:
10.1109/ICECTA48151.2019.8959647.
[55] S. M. Qaisar, R. Yahiaoui, and T. Gharbi, “An efficient signal acquisition with an adaptive rate A/D conversion,” in 2013 IEEE International Conference on Circuits and Systems (ICCAS), 2013, pp. 124–129.
[56] S. M. Qaisar and F. Alsharif, “Event-Driven System for Proficient Load Recognition by Interpreting the Smart Meter Data,” presented at the 2019 Complex Adaptive Systems Conference (CAS), Nov. 2019, p. 8.
[57] S. M. Qaisar, F. Alsharif, A. Subasi, and A. Bensenouci, “Appliance Identification Based on Smart Meter Data and Event–Driven Processing in the 5G,” presented at the 17th International Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, Jan. 2020, p. 7.
[58] S. M. Qaisar and F. Alsharif, “Efficient Detection of Appliance Consumption Pattern by Using Level–Crossing Sampling,” presented at the International Conference on Harmonics and Quality of Power 2020 (ICHQP), Mar. 2020, p. 4.
[59] A. Ridi, C. Gisler, and J. Hennebert, “Appliance and state recognition using Hidden Markov Models,” in 2014 International Conference on Data Science and Advanced Analytics (DSAA), Oct. 2014, pp. 270–276, doi: 10.1109/DSAA.2014.7058084.
[60] “Processing smart plug signals using machine learning - IEEE Conference Publication.” https://ieeexplore.ieee.org/document/7122532 (accessed Jun. 02, 2020).
APPENDICES
Appendix – 1:
A paper that got accepted in the Complex Adaptive Systems (CAS 2019) Conference under the title of “Event–Driven System for Proficient Load Recognition by Interpreting the Smart Meter Data”. The conference was held in Penn State Great Valley Conference Centre between the 13th and 15th of November 2019, and the paper will be published in the Procedia of Computer Science soon.
Appendix – 2:
A paper that got accepted in the 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA 2019) under the title of “ An Adaptive Rate Time–Domain Approach for a Proficient and Automatic Household Appliances Identification ”. The conference took a place between November 19 and 21, 2019 at the American University of Ras Al–Khaimah. The paper has been indexed in IEEE Xplore and Scopus. The following is the citation of the paper:
S. M. Qaisar and F. Alsharif, "An Adaptive Rate Time-Domain Approach for a Proficient and Automatic Household Appliances Identification," 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates, 2019, pp. 1-4.
Appendix – 3 :
Participated in the 9th Saudi Arabia Smart Grid Conference (SASG 2019) which was held in Jeddah between the 10thand12thofDecember 2019. The participation was by a poster under the title of “Event–Driven Time–Domain Method for Effective Identification of Major Appliances by Analyzing Smart–Meter Data”.
Appendix – 4 :
A paper which got accepted in the 17th International Learning and Technology Conference (L & T 2020) under the title of “Appliance Identification Based on Smart Meter Data and Event–Driven Processing in the 5G”. The conference was held in Jeddah, at Effat University and took a place on the 30th of January 2020. The accepted papers will be published in Procedia of Computer Science.