IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i9p9029-9048d54794.html
   My bibliography  Save this article

Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System

Author

Listed:
  • Kofi Afrifa Agyeman

    (Department of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Sekyung Han

    (Department of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Soohee Han

    (Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

Abstract

The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.

Suggested Citation

  • Kofi Afrifa Agyeman & Sekyung Han & Soohee Han, 2015. "Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System," Energies, MDPI, vol. 8(9), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:9029-9048:d:54794
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/9/9029/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/9/9029/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Kempton, Willett & Layne, Linda L., 1994. "The consumer's energy analysis environment," Energy Policy, Elsevier, vol. 22(10), pages 857-866, October.
    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. Samira Ortiz & Mandoye Ndoye & Marcel Castro-Sitiriche, 2021. "Satisfaction-Based Energy Allocation with Energy Constraint Applying Cooperative Game Theory," Energies, MDPI, vol. 14(5), pages 1-18, March.
    2. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
    3. Hui He & Zixuan Liu & Runhai Jiao & Guangwei Yan, 2019. "A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields," Energies, MDPI, vol. 12(9), pages 1-17, May.
    4. 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.
    5. 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.
    6. Hsueh-Hsien Chang & Nguyen Viet Linh, 2017. "Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems," Energies, MDPI, vol. 10(5), pages 1-20, April.
    7. Robertas Lukočius & Žilvinas Nakutis & Vytautas Daunoras & Ramūnas Deltuva & Pranas Kuzas & Roma Račkienė, 2018. "An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids," Energies, MDPI, vol. 12(1), pages 1-26, December.
    8. Augustyn Wójcik & Piotr Bilski & Robert Łukaszewski & Krzysztof Dowalla & Ryszard Kowalik, 2021. "Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator," Energies, MDPI, vol. 14(3), pages 1-26, January.

    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. Astier, Nicolas, 2018. "Comparative feedbacks under incomplete information," Resource and Energy Economics, Elsevier, vol. 54(C), pages 90-108.
    2. Nicolas Astier, 2016. "Comparative Feedbacks under Incomplete Information," Working Papers hal-01465189, HAL.
    3. Chen, Victor L. & Delmas, Magali A. & Kaiser, William J. & Locke, Stephen L., 2015. "What can we learn from high-frequency appliance-level energy metering? Results from a field experiment," Energy Policy, Elsevier, vol. 77(C), pages 164-175.
    4. 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).
    5. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    6. Lang, Corey & Okwelum, Edson, 2015. "The mitigating effect of strategic behavior on the net benefits of a direct load control program," Energy Economics, Elsevier, vol. 49(C), pages 141-148.
    7. Coelho, Igor M. & Coelho, Vitor N. & Luz, Eduardo J. da S. & Ochi, Luiz S. & Guimarães, Frederico G. & Rios, Eyder, 2017. "A GPU deep learning metaheuristic based model for time series forecasting," Applied Energy, Elsevier, vol. 201(C), pages 412-418.
    8. Chow, Sheung Chi & Wenjing, Xu & Xiaoyang, Wu, 2014. "Efficiency of electricity use and productivity change of electricity in China: A nonparametric approach," MPRA Paper 62972, University Library of Munich, Germany.
    9. 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.
    10. 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.
    11. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
    12. Matteo Caldera & Asad Hussain & Sabrina Romano & Valerio Re, 2023. "Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform," Energies, MDPI, vol. 16(2), pages 1-23, January.
    13. 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.
    14. Silvis, Julia & Leighty, Wayne & Karner, Alex, 2007. "Folk Quantification of Transportation Energy: An initial investigation of perceptions of automobile energy use," Institute of Transportation Studies, Working Paper Series qt5xk916rj, Institute of Transportation Studies, UC Davis.
    15. Bladh, Mats & Krantz, Helena, 2008. "Towards a bright future? Household use of electric light: A microlevel study," Energy Policy, Elsevier, vol. 36(9), pages 3521-3530, September.
    16. Quaglione, Davide & Cassetta, Ernesto & Crociata, Alessandro & Sarra, Alessandro, 2017. "Exploring additional determinants of energy-saving behaviour: The influence of individuals' participation in cultural activities," Energy Policy, Elsevier, vol. 108(C), pages 503-511.
    17. Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
    18. 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.
    19. Claudia Bustamante & Stephen Bird & Lisa Legault & Susan E. Powers, 2023. "Energy Hogs and Misers: Magnitude and Variability of Individuals’ Household Electricity Consumption," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    20. Enríquez, R. & Jiménez, M.J. & Heras, M.R., 2017. "Towards non-intrusive thermal load Monitoring of buildings: BES calibration," Applied Energy, Elsevier, vol. 191(C), pages 44-54.

    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:8:y:2015:i:9:p:9029-9048:d:54794. 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.