IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6117-d895680.html
   My bibliography  Save this article

Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats

Author

Listed:
  • Adán Medina

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
    These authors contributed equally to this work.)

  • Juana Isabel Méndez

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
    School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
    These authors contributed equally to this work.)

  • Pedro Ponce

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
    School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
    These authors contributed equally to this work.)

  • Therese Peffer

    (Institute for Energy and Environment, University of California, Berkeley, CA 94720, USA
    These authors contributed equally to this work.)

  • Arturo Molina

    (Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
    School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
    These authors contributed equally to this work.)

Abstract

Energy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO 2 . As a result, some new research areas need to be explored to create innovative energy-saving alternatives in electrical devices that have high energy consumption. One research area of interest is the computer visual classification for reducing energy consumption and keeping thermal comfort in thermostats. Usually, connected thermostats obrtain information from sensors for detecting persons and scheduling autonomous operations to save energy. However, there is a lack of knowledge of how computer vision can be deployed in embedded digital systems to analyze clothing insulation in connected thermostats to reduce energy consumption and keep thermal comfort. The clothing classification algorithm embedded in a digital system for saving energy could be a companion device in connected thermostats to obtain the clothing insulation. Currently, there is no connected thermostat in the market using complementary computer visual classification systems to analyze the clothing insulation factor. Hence, this proposal aims to develop and evaluate an embedded real-time clothing classifier that could help to improve the efficiency of heating and ventilation air conditioning systems in homes or buildings. This paper compares six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models. In addition, the paper describes how the classifier could interact with the thermostat to tune the temperature set point to save energy and keep thermal comfort. The results confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy.

Suggested Citation

  • Adán Medina & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Arturo Molina, 2022. "Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats," Energies, MDPI, vol. 15(17), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6117-:d:895680
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6117/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/17/6117/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manuel Avila & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2021. "Energy Management System Based on a Gamified Application for Households," Energies, MDPI, vol. 14(12), pages 1-27, June.
    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. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.
    2. Rima Aridi & Jalal Faraj & Samer Ali & Mostafa Gad El-Rab & Thierry Lemenand & Mahmoud Khaled, 2021. "Energy Recovery in Air Conditioning Systems: Comprehensive Review, Classifications, Critical Analysis, and Potential Recommendations," Energies, MDPI, vol. 14(18), pages 1-31, September.
    3. Adán Medina & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats," Energies, MDPI, vol. 15(5), pages 1-28, March.
    4. Omar Mata & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2023. "Energy Savings in Buildings Based on Image Depth Sensors for Human Activity Recognition," Energies, MDPI, vol. 16(3), pages 1-22, January.

    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:15:y:2022:i:17:p:6117-:d:895680. 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.