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Non-intrusive load monitoring through home energy management systems: A comprehensive review

Author

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  • Hosseini, Sayed Saeed
  • Agbossou, Kodjo
  • Kelouwani, Sousso
  • Cardenas, Alben

Abstract

The enhanced utilization of Appliance Load Monitoring (ALM) in customer sites enabled by Home Energy Management Systems (HEMS) technologies, offers customized services and enables demand side flexibility in power systems. The significant integration of advanced electrical and computer engineering tools makes the nonintrusive approach of ALM a technically feasible solution to improve demand side energy utilization in the context of HEMS. This paper presents a comprehensive study conducted to reveal significant inevitabilities of a well organized Non-intrusive Load Monitoring (NILM) that aids Smart Home (SH) idea to be implemented. In fact, the viewpoint of this study is to discuss critical issues related to NILM prerequisite necessities, hindered the practical implication of this approach despite improvements during over 30 years. Accordingly, this work presents actual analyses in order to elucidate some arguments using state of the art procedures and results of a semi-synthetic data generator tool. In addition, with the aim of an achievable NILM, we analyze NILM applications from the stakeholders’ perspective to assist the choice of employed techniques. Consequently, by investigating crucial intentions of an effective NILM considering current standstill and future progression, the authors propose the Advanced NILM (ANILM) concept and describe its properties to provide an enhanced energy usage system in demand side. In order to meet its ambition, the paper uses a realistic point of view to pinpoint major obstacles toward NILM and elaborate various factors that will make it effectively feasible.

Suggested Citation

  • Hosseini, Sayed Saeed & Agbossou, Kodjo & Kelouwani, Sousso & Cardenas, Alben, 2017. "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1266-1274.
    • Handle: RePEc:eee:rensus:v:79:y:2017:i:c:p:1266-1274
    DOI: 10.1016/j.rser.2017.05.096
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    References listed on IDEAS

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    1. Flavio Martins & Maria Fatima Almeida & Rodrigo Calili & Agatha Oliveira, 2020. "Design Thinking Applied to Smart Home Projects: A User-Centric and Sustainable Perspective," Sustainability, MDPI, vol. 12(23), pages 1-27, December.
    2. Younghoon Kwak & Jihyun Hwang & Taewon Lee, 2018. "Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building," Energies, MDPI, vol. 11(4), pages 1-22, April.
    3. Muhammad Majid Hussain & Rizwan Akram & Zulfiqar Ali Memon & Mian Hammad Nazir & Waqas Javed & Muhammad Siddique, 2021. "Demand Side Management Techniques for Home Energy Management Systems for Smart Cities," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    4. Lemos-Vinasco, Julian & Bacher, Peder & Møller, Jan Kloppenborg, 2021. "Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households’ electrical load," Applied Energy, Elsevier, vol. 303(C).
    5. Omar Alrawi & I. Safak Bayram & Sami G. Al-Ghamdi & Muammer Koc, 2019. "High-Resolution Household Load Profiling and Evaluation of Rooftop PV Systems in Selected Houses in Qatar," Energies, MDPI, vol. 12(20), pages 1-25, October.
    6. Liu, Yu & Liu, Wei & Shen, Yiwen & Zhao, Xin & Gao, Shan, 2021. "Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations," Applied Energy, Elsevier, vol. 287(C).
    7. Paoli, Leonardo & Lupton, Richard C. & Cullen, Jonathan M., 2018. "Useful energy balance for the UK: An uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 176-188.
    8. Zheng, Zhuang & Sun, Zhankun & Pan, Jia & Luo, Xiaowei, 2021. "An integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems," Applied Energy, Elsevier, vol. 298(C).
    9. Netzah Calamaro & Moshe Donko & Doron Shmilovitz, 2021. "A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements," Energies, MDPI, vol. 14(21), pages 1-37, November.
    10. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
    11. Zhuang Zheng & Hainan Chen & Xiaowei Luo, 2018. "A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances," Sustainability, MDPI, vol. 10(4), pages 1-28, March.
    12. Farhad Farokhi, 2019. "Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon," Papers 1908.03995, arXiv.org, revised Jan 2020.
    13. Liu, Yu & Liu, Congxiao & Ling, Qicheng & Zhao, Xin & Gao, Shan & Huang, Xueliang, 2021. "Toward smart distributed renewable generation via multi-uncertainty featured non-intrusive interactive energy monitoring," Applied Energy, Elsevier, vol. 303(C).
    14. Debnath, Ramit & Bardhan, Ronita & Misra, Ashwin & Hong, Tianzhen & Rozite, Vida & Ramage, Michael H., 2022. "Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models," Energy Policy, Elsevier, vol. 164(C).
    15. Marikyan, Davit & Papagiannidis, Savvas & Alamanos, Eleftherios, 2019. "A systematic review of the smart home literature: A user perspective," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 139-154.
    16. Luan, Wenpeng & Tian, Longfei & Zhao, Bochao, 2023. "Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design," Applied Energy, Elsevier, vol. 342(C).
    17. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    18. Ce Peng & Guoying Lin & Shaopeng Zhai & Yi Ding & Guangyu He, 2020. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model," Energies, MDPI, vol. 13(21), pages 1-19, October.
    19. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    20. Cristina Puente & Rafael Palacios & Yolanda González-Arechavala & Eugenio Francisco Sánchez-Úbeda, 2020. "Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques," Energies, MDPI, vol. 13(12), pages 1-20, June.
    21. Raphael Iten & Joël Wagner & Angela Zeier Röschmann, 2021. "On the Identification, Evaluation and Treatment of Risks in Smart Homes: A Systematic Literature Review," Risks, MDPI, vol. 9(6), pages 1-30, June.
    22. Shimoda, Yoshiyuki & Yamaguchi, Yohei & Iwafune, Yumiko & Hidaka, Kazuyoshi & Meier, Alan & Yagita, Yoshie & Kawamoto, Hisaki & Nishikiori, Soichi, 2020. "Energy demand science for a decarbonized society in the context of the residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    23. Toro-Cárdenas, Mateo & Moreira, Inês & Morais, Hugo & Carvalho, Pedro M.S. & Ferreira, Luis A.F.M., 2023. "Net load disaggregation at secondary substation level," Renewable Energy, Elsevier, vol. 207(C), pages 765-771.
    24. Tomasz Jasiński, 2020. "Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)," Energies, MDPI, vol. 13(5), pages 1-16, March.
    25. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.

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