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Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review

Author

Listed:
  • Amjad Almusaed

    (Department of Construction Engineering and Lighting Science, Jonkoping University, 553 18 Jonkoping, Sweden)

  • Ibrahim Yitmen

    (Department of Construction Engineering and Lighting Science, Jonkoping University, 553 18 Jonkoping, Sweden)

  • Asaad Almssad

    (Department of Building Technology, Karlstad University, 651 88 Karlstad, Sweden)

Abstract

The normal development of “smart buildings,” which calls for integrating sensors, rich data, and artificial intelligence (AI) simulation models, promises to usher in a new era of architectural concepts. AI simulation models can improve home functions and users’ comfort and significantly cut energy consumption through better control, increased reliability, and automation. This article highlights the potential of using artificial intelligence (AI) models to improve the design and functionality of smart houses, especially in implementing living spaces. This case study provides examples of how artificial intelligence can be embedded in smart homes to improve user experience and optimize energy efficiency. Next, the article will explore and thoroughly analyze the thorough analysis of current research on the use of artificial intelligence (AI) technology in smart homes using a variety of innovative ideas, including smart interior design and a Smart Building System Framework based on digital twins (DT). Finally, the article explores the advantages of using AI models in smart homes, emphasizing living spaces. Through the case study, the theme seeks to provide ideas on how AI can be effectively embedded in smart homes to improve functionality, convenience, and energy efficiency. The overarching goal is to harness the potential of artificial intelligence by transforming how we live in our homes and improving our quality of life. The article concludes by discussing the unresolved issues and potential future research areas on the usage of AI in smart houses. Incorporating AI technology into smart homes benefits homeowners, providing excellent safety and convenience and increased energy efficiency.

Suggested Citation

  • Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review," Energies, MDPI, vol. 16(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2636-:d:1094089
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    References listed on IDEAS

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    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Sovacool, Benjamin K. & Martiskainen, Mari & Furszyfer Del Rio, Dylan D., 2021. "Knowledge, energy sustainability, and vulnerability in the demographics of smart home technology diffusion," Energy Policy, Elsevier, vol. 153(C).
    3. Anatolijs Borodinecs & Arturs Palcikovskis & Vladislavs Jacnevs, 2022. "Indoor Air CO 2 Sensors and Possible Uncertainties of Measurements: A Review and an Example of Practical Measurements," Energies, MDPI, vol. 15(19), pages 1-15, September.
    4. Asaad Almssad & Amjad Almusaed & Raad Z. Homod, 2022. "Masonry in the Context of Sustainable Buildings: A Review of the Brick Role in Architecture," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    5. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    6. Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    7. Muhammad Abbas Khan & Ijaz Ahmad & Anis Nurashikin Nordin & A. El-Sayed Ahmed & Hiren Mewada & Yousef Ibrahim Daradkeh & Saim Rasheed & Elsayed Tag Eldin & Muhammad Shafiq, 2022. "Smart Android Based Home Automation System Using Internet of Things (IoT)," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
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    Cited by:

    1. Amjad Almusaed & Asaad Almssad & Asaad Alasadi & Ibrahim Yitmen & Sammera Al-Samaraee, 2023. "Assessing the Role and Efficiency of Thermal Insulation by the “BIO-GREEN PANEL” in Enhancing Sustainability in a Built Environment," Sustainability, MDPI, vol. 15(13), pages 1-25, July.
    2. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments," Sustainability, MDPI, vol. 15(18), pages 1-27, September.

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