IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1452-d1358864.html
   My bibliography  Save this article

Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning

Author

Listed:
  • Fernando Ulloa-Vásquez

    (Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800002, Chile)

  • Victor Heredia-Figueroa

    (Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile)

  • Cristóbal Espinoza-Iriarte

    (Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile)

  • José Tobar-Ríos

    (Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile)

  • Fernanda Aguayo-Reyes

    (Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile)

  • Dante Carrizo

    (Departamento Ing. Informatica y Cs. de la Computación, Facultad de Ingeniería, Universidad de Atacama, Copiapó 1531772, Chile)

  • Luis García-Santander

    (Departamento de Ingeniería Eléctrica, Universidad de Concepción, Concepción 4089100, Chile)

Abstract

The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals to obtain a home energy management system (HEMS), which allows reducing the use of electrical energy. This article incorporates artificial intelligence techniques to demand response, allowing control, switching, turning on and off of appliances, modifying and reducing consumption, and achieving improvements in the quality of life in the home. In addition, an architecture based on a smart socket and an artificial intelligence model that recognizes the consumption of electrical appliances in high resolution (sampling every 10 s) is proposed. The system uses the Wi-Fi communication protocol, ensuring that the smart sockets wirelessly provide the data obtained to the public cloud. The use of Deep Learning allows us to obtain a central control model of the home, which, when interconnected to the smart electrical distribution networks of companies, could generate a positive impact on the environmental effects and CO 2 reduction.

Suggested Citation

  • Fernando Ulloa-Vásquez & Victor Heredia-Figueroa & Cristóbal Espinoza-Iriarte & José Tobar-Ríos & Fernanda Aguayo-Reyes & Dante Carrizo & Luis García-Santander, 2024. "Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning," Energies, MDPI, vol. 17(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1452-:d:1358864
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1452/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/6/1452/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data," Energies, MDPI, vol. 8(7), pages 1-21, July.
    Full references (including those not matched with items on IDEAS)

    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. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    2. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    3. Qadrdan, Meysam & Fazeli, Reza & Jenkins, Nick & Strbac, Goran & Sansom, Robert, 2019. "Gas and electricity supply implications of decarbonising heat sector in GB," Energy, Elsevier, vol. 169(C), pages 50-60.
    4. 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.
    5. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    6. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    7. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    8. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    9. Yazhou Jiang & Chen-Ching Liu & Yin Xu, 2016. "Smart Distribution Systems," Energies, MDPI, vol. 9(4), pages 1-20, April.
    10. Akito Ozawa & Ryota Furusato & Yoshikuni Yoshida, 2017. "Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan," Sustainability, MDPI, vol. 9(4), pages 1-23, March.

    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:17:y:2024:i:6:p:1452-:d:1358864. 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.