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Home Energy Management Systems (HEMSs) with Optimal Energy Management of Home Appliances Using IoT

Author

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  • Hyung-Chul Jo

    (Energy Platform Research Center, Korea Electrotechnology Research Institute, 27, Dosicheomdansaneop-ro, Nam-gu, Gwangju 61751, Republic of Korea)

  • Hyang-A Park

    (Energy Platform Research Center, Korea Electrotechnology Research Institute, 27, Dosicheomdansaneop-ro, Nam-gu, Gwangju 61751, Republic of Korea)

  • Soon-Young Kwon

    (Energy Platform Research Center, Korea Electrotechnology Research Institute, 27, Dosicheomdansaneop-ro, Nam-gu, Gwangju 61751, Republic of Korea)

  • Kyeong-Hee Cho

    (Energy Platform Research Center, Korea Electrotechnology Research Institute, 27, Dosicheomdansaneop-ro, Nam-gu, Gwangju 61751, Republic of Korea)

Abstract

Home appliances connected to Internet-of-Things (IoT) platforms have been extensively installed in smart homes. In this context, home energy management systems (HEMSs) have emerged as a viable solution for reducing energy costs. Although several studies have analyzed the implementation of HEMSs, a majority of these studies were based on the installation of numerous sensors. Owing to the complexity and costs associated with the installation of multiple sensors, implementation of HEMSs in smart homes is challenging. This paper presents an energy management scheme for an HEMS that minimizes the energy cost in a smart home with data obtained from IoT-based appliances typically used in a smart home. Case studies were conducted to demonstrate the effectiveness of the proposed method. Prototype software (version 11) based on the proposed method for a HEMS and a test house with IoT-based appliances were also implemented in the case studies.

Suggested Citation

  • Hyung-Chul Jo & Hyang-A Park & Soon-Young Kwon & Kyeong-Hee Cho, 2024. "Home Energy Management Systems (HEMSs) with Optimal Energy Management of Home Appliances Using IoT," Energies, MDPI, vol. 17(12), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:3009-:d:1417467
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    References listed on IDEAS

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    3. Zhu, Yingzi & Zhou, Guofu, 2009. "Technical analysis: An asset allocation perspective on the use of moving averages," Journal of Financial Economics, Elsevier, vol. 92(3), pages 519-544, June.
    4. Karol Bot & Inoussa Laouali & António Ruano & Maria da Graça Ruano, 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques," Energies, MDPI, vol. 14(18), pages 1-27, September.
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    Cited by:

    1. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko & Tomasz Bednarek & Krzysztof Tyburek, 2025. "Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study," Energies, MDPI, vol. 18(7), pages 1-28, March.

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