IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p189-d126713.html
   My bibliography  Save this article

Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review

Author

Listed:
  • Anwar Ul Haq

    (Department of Informatics (I-13), Bolzmanstr. 3, Technical University Munich, 85748 Garching, Germany)

  • Hans-Arno Jacobsen

    (Department of Informatics (I-13), Bolzmanstr. 3, Technical University Munich, 85748 Garching, Germany)

Abstract

The smart grid initiative has encouraged utility companies worldwide to roll-out new and smarter versions of energy meters. Before an extensive roll-out, which is both labor-intensive and incurs high capital costs, consumers need to be incentivised to reap the long-term benefits of such smart meters. Off-the-shelf energy monitors (e-monitors) can provide consumers with an insight into such potential benefits. As e-monitors are owned by the consumer, the consumer has greater control over the data, which significantly reduces the privacy and data confidentiality concerns. Because only limited online technical information is available about e-monitors, we evaluate several existing e-monitors using an online technical survey directly from the vendors. Besides automated e-monitoring, the use of different off-the-shelf e-monitors can also help to demonstrate state-of-the-art techniques such as non-intrusive load monitoring (NILM), data analytics, and the predictive maintenance of appliances. Our survey indicates a trend towards the incorporation of such state-of-the-art capabilities, particularly the appliance-level e-monitoring and load disaggregation. We have also discussed some essential requirements to implement load disaggregation in the next generation e-monitors. In future, these intelligent e-monitoring techniques will encourage effective consumer participation in the demand-side management (DSM) programs.

Suggested Citation

  • Anwar Ul Haq & Hans-Arno Jacobsen, 2018. "Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review," Energies, MDPI, vol. 11(1), pages 1-22, January.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:189-:d:126713
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/189/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/1/189/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
    2. Li, Rongling & Dane, Gamze & Finck, Christian & Zeiler, Wim, 2017. "Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands," Applied Energy, Elsevier, vol. 203(C), pages 623-634.
    3. Hamed Nabizadeh Rafsanjani & Changbum R. Ahn & Mahmoud Alahmad, 2015. "A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings," Energies, MDPI, vol. 8(10), pages 1-34, October.
    4. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    5. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    6. Aggelos S. Bouhouras & Paschalis A. Gkaidatzis & Konstantinos C. Chatzisavvas & Evangelos Panagiotou & Nikolaos Poulakis & Georgios C. Christoforidis, 2017. "Load Signature Formulation for Non-Intrusive Load Monitoring Based on Current Measurements," Energies, MDPI, vol. 10(4), pages 1-21, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hari Prasad Devarapalli & V. S. S. Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2020. "Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. 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).
    3. 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.
    4. Yuval Beck & Ram Machlev, 2019. "Harmonic Loads Classification by Means of Currents’ Physical Components," Energies, MDPI, vol. 12(21), pages 1-18, October.
    5. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pascal A. Schirmer & Iosif Mporas, 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    2. Liu, Bo & Luan, Wenpeng & Yu, Yixin, 2017. "Dynamic time warping based non-intrusive load transient identification," Applied Energy, Elsevier, vol. 195(C), pages 634-645.
    3. Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.
    4. Hasan Rafiq & Xiaohan Shi & Hengxu Zhang & Huimin Li & Manesh Kumar Ochani, 2020. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing," Energies, MDPI, vol. 13(9), pages 1-26, May.
    5. Sara Tavakoli & Kaveh Khalilpour, 2021. "A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability," Energies, MDPI, vol. 14(16), pages 1-27, August.
    6. Bonfigli, Roberto & Principi, Emanuele & Fagiani, Marco & Severini, Marco & Squartini, Stefano & Piazza, Francesco, 2017. "Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models," Applied Energy, Elsevier, vol. 208(C), pages 1590-1607.
    7. 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.
    8. Hari Prasad Devarapalli & V. S. S. Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2020. "Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion," Energies, MDPI, vol. 13(18), pages 1-15, September.
    9. Esa, Nur Farahin & Abdullah, Md Pauzi & Hassan, Mohammad Yusri, 2016. "A review disaggregation method in Non-intrusive Appliance Load Monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 163-173.
    10. 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.
    11. 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).
    12. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    13. Cheng, Meng & Sami, Saif Sabah & Wu, Jianzhong, 2017. "Benefits of using virtual energy storage system for power system frequency response," Applied Energy, Elsevier, vol. 194(C), pages 376-385.
    14. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    15. Chatzigeorgiou, I.M. & Andreou, G.T., 2021. "A systematic review on feedback research for residential energy behavior change through mobile and web interfaces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    16. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach," HSC Research Reports HSC/14/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    17. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    18. Daví-Arderius, Daniel & Sanin, María-Eugenia & Trujillo-Baute, Elisa, 2017. "CO2 content of electricity losses," Energy Policy, Elsevier, vol. 104(C), pages 439-445.
    19. Claire M. Weiller & Michael G. Pollitt, 2013. "Platform markets and energy services," Working Papers EPRG 1334, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    20. Costa-Campi, Maria Teresa & Daví-Arderius, Daniel & Trujillo-Baute, Elisa, 2018. "The economic impact of electricity losses," Energy Economics, Elsevier, vol. 75(C), pages 309-322.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:189-:d:126713. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.