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An advanced smart home energy management system considering identification of ADLs based on non-intrusive load monitoring

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Abstract

The key advantage of power utility-owned smart meters over rotating-disc meters is the ability of transmitting electrical energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electrical energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence make plentiful consumer-centric use cases possible. One of the plentiful consumer-centric use cases is detection and classification of household activities of daily livings (ADLs). A smart meter can acquire composite/circuit-level electrical energy consumption, but it cannot disaggregate its acquired composite/circuit-level electrical energy consumption into appliance-level electrical energy consumption. In this paper, a non-context-aware human life-pattern identification mechanism based on non-intrusive load monitoring (NILM) is presented for detection and classification of household ADLs. Here, NILM disaggregates composite/circuit-level electrical energy consumption into appliance-level electrical energy consumption with no intrusive deployment of networked plug-level power meters as the fundamental constituents of a traditional home energy management system, and it automatically characterizes physical characteristics of relevant electrical appliances for detection and classification of household ADLs without user intervention.

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References

  1. Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of Things for smart cities. IEEE Internet Things J 1:22–32

    Article  Google Scholar 

  2. Liao CF, Chen PY (2017) ROSA: Resource-oriented service management schemes for Web of Things in a smart home. Sensors 17:2159

    Article  Google Scholar 

  3. Alilou M, Tousi B, Shayeghi H (2021) Multi-objective energy management of smart homes considering uncertainty in wind power forecasting. Electr Eng 103:1367–1383. https://doi.org/10.1007/s00202-020-01165-6

    Article  Google Scholar 

  4. Lin Y-H (2019) Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand-side management. Int Trans Electr Energy Syst 29:e12014. https://doi.org/10.1002/2050-7038.12014

    Article  Google Scholar 

  5. Elsayed AM, Hegab MM, Farrag SM (2019) Smart residential load management technique for distribution systems’ performance enhancement and consumers’ satisfaction achievement. Int Trans Electr Energy Syst 29:e2795. https://doi.org/10.1002/etep.2795

    Article  Google Scholar 

  6. Hannan MA, Faisal M, Ker PJ, Mun LH, Parvin K, Mahlia TMI, Blaabjerg F (2018) A review of Internet of Energy based building energy management systems: issues and recommendations. IEEE Access 6:38997–39014

    Article  Google Scholar 

  7. Moghaddam MHY, Leon-Garcia A (2018) A fog-based Internet of Energy architecture for transactive energy management systems. IEEE Internet Things J 5:1055–1069

    Article  Google Scholar 

  8. Nguyen VT, Vu TL, Le NT, Jang YM (2018) An overview of Internet of Energy (IoE) based building energy management system. In: Proceedings of the 9th international conference on information and communication technology convergence (ICTC 2018), Jeju Island, South Korea, pp 852–855

  9. Völker B, Reinhardt A, Faustine A, Pereira L (2021) Watt’s up at home? Smart meter data analytics from a consumer-centric perspective. Energies 14:719

    Article  Google Scholar 

  10. Tundis A, Faizan A, Mühlhäuser M (2019) A feature-based model for the identification of electrical devices in smart environments. Sensors 19:2611

    Article  Google Scholar 

  11. Lin YH, Tsai MS (2015) An advanced home energy management system facilitated by nonintrusive load monitoring with automated multiobjective power scheduling. IEEE Trans Smart Grid 6:1839–1851

    Article  Google Scholar 

  12. Lin YH, Hu YC (2018) Residential consumer-centric demand-side management based on energy disaggregation-piloting constrained swarm intelligence: towards edge computing. Sensors 18:1365

    Article  Google Scholar 

  13. Devlin MA, Hayes BP (2019) Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data. IEEE Trans Consum Electron 65:339–348

    Article  Google Scholar 

  14. Li D, Dick S (2021) Non-intrusive load monitoring using multi-label classification methods. Electr Eng 103:607–619. https://doi.org/10.1007/s00202-020-01078-4

    Article  Google Scholar 

  15. Chalmers C, Fergus P, Curbelo Montanez CA, Sikdar S, Ball F, Kendall B (2020) Detecting activities of daily living and routine behaviours in dementia patients living alone using smart meter load disaggregation. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/TETC.2020.2993177

    Article  Google Scholar 

  16. Hu YC, Lin YH, Lin C-H (2020) Artificial Intelligence, accelerated in parallel computing and applied to nonintrusive appliance load monitoring for residential demand-side management in a smart grid: a comparative study. Appl Sci 10:8114

    Article  Google Scholar 

  17. Taveira PRZ, Moraes CHV, de; Lambert-Torres, G. (2020) Non-intrusive identification of loads by random forest and fireworks optimization. IEEE Access 8:75060–75072

    Article  Google Scholar 

  18. Lu M, Li Z (2020) A hybrid event detection approach for non-intrusive load monitoring. IEEE Trans Smart Grid 11:528–540

    Article  Google Scholar 

  19. Le T-T-H, Kang H, Kim H (2020) Household appliance classification using lower odd-numbered harmonics and the bagging decision tree. IEEE Access 8:55937–55952

    Article  Google Scholar 

  20. Alcalá JM, Ureña J, Hernández Á, Gualda D (2017) Sustainable homecare monitoring system by sensing electricity data. IEEE Sens J 17:7741–7749

    Article  Google Scholar 

  21. Franco P, Martinez JM, Kim YC, Ahmed MA (2021) IoT based approach for load monitoring and activity recognition in smart homes. IEEE Access 9:45325–45339

    Article  Google Scholar 

  22. McKenna E, Richardson I, Thomson M (2012) Smart meter data: balancing consumer privacy concerns with legitimate applications. Energy Policy 41:807–814. https://doi.org/10.1016/j.enpol.2011.11.049

    Article  Google Scholar 

  23. Ridi A, Gisler C, Hennebert J (2014) A survey on intrusive load monitoring for appliance recognition. In: Proceedings of the 22nd international conference on pattern recognition (ICPR 2014), Stockholm, Sweden, pp 3702–3707

  24. Chen WH, Tseng WS (2007) A novel multi-agent framework for the design of home automation. In: Proceedings of the fourth international conference on information technology (ITNG’07), Las Vegas, NV, USA, pp 277–281

  25. Chen WH, Tseng WS (2007) An agent model for the control of smart appliances. In: Proceedings of 2007 annual meeting of the North American Fuzzy Information Processing Society (NAFIPS’07), San Diego, CA, USA, pp 95–99

  26. Fahad LG, Tahir SF (2021) Activity recognition and anomaly detection in smart homes. Neurocomputing 423:362–372

    Article  Google Scholar 

  27. Amini MH, Nabi B, Haghifam MR (2013) Load management using multi-agent systems in smart distribution network. In: Proceedings of the 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada, pp 1–5

  28. Bahrami S, Wong VWS (2015) An autonomous demand response program in smart grid with foresighted users. In: Proceedings of the 6th IEEE international conference on smart grid communications (SmartGridComm 2015), Miami, FL, USA, pp 205–210

  29. Lin YH (2021) Trainingless multi-objective evolutionary computing-based nonintrusive load monitoring: part of smart-home energy management for demand-side management. J Build Eng 33:101601

    Article  Google Scholar 

  30. Rafsanjani HN, Ghahramani A (2019) Extracting occupants’ energy-use patterns from Wi-Fi networks in office buildings. J Build Eng 26:100864

    Article  Google Scholar 

  31. Welcome to Python.org. Available online: https://www.python.org/. Accessed on 22 Feb 2021

  32. Scikit-learn: machine learning in Python. Available online: https://scikit-learn.org. Accessed on 2 Feb 2021

  33. Chang HH, Chen KL, Tsai YP, Lee WJ (2012) A new measurement method for power signatures of non-intrusive demand monitoring and load identification. IEEE Trans Ind Appl 48:764–771

    Article  Google Scholar 

  34. Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6:21–45

    Article  Google Scholar 

  35. Dietterich TG (2020) An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Mach Learn 40:139–157

    Article  Google Scholar 

  36. Kumar S (2013) Modified C4.5 algorithm with improved information entropy and gain ratio. Int J Eng Res Technol 2:2768–2773

    Google Scholar 

  37. Machine Learning Crash Course|Google Developers. Available online: https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative; https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall; https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc. Accessed on 09 March 2021

  38. Srinivasan D, Ng WS, Liew AC (2006) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Deliv 21:398–405

    Article  Google Scholar 

  39. Li L, Yang L, Chen H, Li M, Zhang C (2019) Multi-objective evolutionary algorithms applied to non-intrusive load monitoring. Electr Power Syst Res 177:105961

    Article  Google Scholar 

  40. Devarapalli HP, Dhanikonda VSSSS, Gunturi SB (2020) Non-intrusive identification of load patterns in smart homes using percentage total harmonic distortion. Energies 13:4628. https://doi.org/10.3390/en13184628

    Article  Google Scholar 

  41. sklearn.ensemble.BaggingClassifier. Available online: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html. Accessed on 29 March 2021

  42. sklearn.model_selection.GridSearchCV. Available online: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html. Accessed on 29 March 2021

  43. sklearn.model_selection.train_test_split. Available online: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html. Accessed on 29 March 2021

  44. Deng JL (1982) Control problems of grey systems. Syst Contr Lett 1:288–294

    Article  MathSciNet  Google Scholar 

  45. Deng JL (1989) Introduction to grey system theory. J Grey Syst 1:1–24

    MathSciNet  MATH  Google Scholar 

  46. Huang YP, Huang CC (1996) The integration and application of fuzzy and grey modeling methods. Fuzzy Sets Syst 78:107–119

    Article  MathSciNet  Google Scholar 

  47. Chen YY, Lin YH (2019) A smart autonomous time- and frequency-domain analysis current sensor-based power meter prototype developed over fog-cloud analytics for demand-side management. Sensors 19:4443. https://doi.org/10.3390/s19204443

    Article  Google Scholar 

  48. Chen WH, Chiu TC, Chen WH (2018) Implementation of in-home activity recognition based on web service and machine learning approaches. In: Proceedings of the 6th IIAE international conference on intelligent systems and image processing (ICISIP 2018), Matsue, Japan, pp 91–96

  49. Chen WH, Baca CAB, Tou CH (2017) LSTM-RNNs combined with scene information for human activity recognition. In: Proceedings of the 19th IEEE international conference of the e-Health Netw. Appl. Serv. (Healthcom), Dalian, China, pp 1–6

  50. Wang A, Chen G, Yang J, Zhao S, Chang C (2016) A comparative study on human activity recognition using inertial sensors in a smartphone. IEEE Sens J 16:4566–4578

    Article  Google Scholar 

Download references

Acknowledgements

This paper was supported in part by the Ministry of Science and Technology, Taiwan, under the Grant Numbers MOST 109-2221-E-027-121-MY2, MOST 110-3116-F-006-001- and MOST 110-3116-F-027-001-. The author would like to thank the reviewers and editor for their valuable, insightful comments and suggestions on this paper.

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  1. Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei, 106344, Taiwan

    Yu-Hsiu Lin

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Lin, YH. An advanced smart home energy management system considering identification of ADLs based on non-intrusive load monitoring. Electr Eng 104, 3391–3409 (2022). https://doi.org/10.1007/s00202-022-01546-z

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