IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i12p3009-d1417467.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/12/3009/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/12/3009/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yizhen Wang & Ningqing Zhang & Xiong Chen, 2021. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features," Energies, MDPI, vol. 14(10), pages 1-13, May.
    2. 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.
    3. 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.
    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. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    2. Shi Yafeng & Tao Xiangxing & Shi Yanlong & Zhu Nenghui & Ying Tingting & Peng Xun, 2020. "Can Technical Indicators Provide Information for Future Volatility: International Evidence," Journal of Systems Science and Information, De Gruyter, vol. 8(1), pages 53-66, February.
    3. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    4. Erdemlioglu, Deniz & Petitjean, Mikael & Vargas, Nicolas, 2021. "Market instability and technical trading at high frequency: Evidence from NASDAQ stocks," Economic Modelling, Elsevier, vol. 102(C).
    5. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    6. Ben R. Marshall & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2017. "Time series momentum and moving average trading rules," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 405-421, March.
    7. Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021. "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    8. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    9. K. J. Hong & S. Satchell, 2015. "Time series momentum trading strategy and autocorrelation amplification," Quantitative Finance, Taylor & Francis Journals, vol. 15(9), pages 1471-1487, September.
    10. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.
    11. Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.
    12. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    13. Ryan Flugum, 2021. "The trend is an analyst's friend: Analyst recommendations and market technicals," The Financial Review, Eastern Finance Association, vol. 56(2), pages 301-330, May.
    14. Jin, Xiaoye, 2021. "What do we know about the popularity of technical analysis in foreign exchange markets? A skewness preference perspective," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    15. Santos, André A.P. & Torrent, Hudson S., 2022. "Markowitz meets technical analysis: Building optimal portfolios by exploiting information in trend-following signals," Finance Research Letters, Elsevier, vol. 49(C).
    16. Zheng Wan & Hui Li, 2023. "Short-Term Power Load Forecasting Based on Feature Filtering and Error Compensation under Imbalanced Samples," Energies, MDPI, vol. 16(10), pages 1-22, May.
    17. Chen, Xingjiang & Ruan, Xinfeng & Zhang, Wenjun, 2021. "Dynamic portfolio choice and information trading with recursive utility," Economic Modelling, Elsevier, vol. 98(C), pages 154-167.
    18. Yafeng Qin & Guoyao Pan & Min Bai, 2020. "Improving market timing of time series momentum in the Chinese stock market," Applied Economics, Taylor & Francis Journals, vol. 52(43), pages 4711-4725, September.
    19. Ansari Saleh Ahmar, 2019. "Sutte Indicator: an approach to predict the direction of stock market movements," Papers 1903.11642, arXiv.org.
    20. Gianluca Fusai & Ioannis Kyriakou, 2016. "General Optimized Lower and Upper Bounds for Discrete and Continuous Arithmetic Asian Options," Mathematics of Operations Research, INFORMS, vol. 41(2), pages 531-559, May.

    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:12:p:3009-:d:1417467. 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.