IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i12p5047-d1414227.html
   My bibliography  Save this article

Design and Experimental Results of an AIoT-Enabled, Energy-Efficient Ceiling Fan System

Author

Listed:
  • Hashim Raza Khan

    (Faculty of Electrical and Computer Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan
    Neurocomputation Lab (National Centre of Artificial Intelligence—NCAI), NED University of Engineering & Technology, Karachi 75270, Pakistan)

  • Wajahat Ahmed

    (Neurocomputation Lab (National Centre of Artificial Intelligence—NCAI), NED University of Engineering & Technology, Karachi 75270, Pakistan)

  • Wasiq Masud

    (Neurocomputation Lab (National Centre of Artificial Intelligence—NCAI), NED University of Engineering & Technology, Karachi 75270, Pakistan)

  • Urooj Alam

    (Neurocomputation Lab (National Centre of Artificial Intelligence—NCAI), NED University of Engineering & Technology, Karachi 75270, Pakistan)

  • Kamran Arshad

    (Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates
    Artificial Intelligence Research Center, Ajman University, Ajman P.O. Box 346, United Arab Emirates)

  • Khaled Assaleh

    (Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates
    Artificial Intelligence Research Center, Ajman University, Ajman P.O. Box 346, United Arab Emirates)

  • Saad Ahmed Qazi

    (Faculty of Electrical and Computer Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan
    Neurocomputation Lab (National Centre of Artificial Intelligence—NCAI), NED University of Engineering & Technology, Karachi 75270, Pakistan)

Abstract

With technological advancements, domestic appliances are leveraging smart technologies for getting smarter through learning from their past usage to enhance user comfort and energy efficiency. Among these, ceiling fans, though widely used in Lower- and Middle-Income Countries (LMICs) in temperate regions, still lack a cohesive system integrating all necessary sensors with a machine learning-based system to optimize their operation for comfort and energy saving and to experimentally verify the performance under different usage scenarios that could transform a high-power-consuming device into an energy-efficient system. Therefore, the present research proposes an experimentally verified and energy-efficient Artificial Intelligence of Things (AIoT)-based system that could be retrofitted with regular DC ceiling fans. An Internet of Things (IoTs) circuit, equipped with an ESP8266 microcontroller, temperature, humidity, and motion sensors, was designed to communicate with a developed Android application and an online dashboard. A total of 123 ceiling fans with the designed IoTs circuit were deployed at various household locations for two years, with manual operations for the first year. In the next year, an auto mode based on the predictions of the machine learning model was introduced. The experimental outcomes showed that the fan with added smart features reduced the energy loss by almost 50% as compared to conventional AC ceiling fans. Consequently, the carbon footprint of the appliances is reduced significantly. A high user-rated acceptability of the system, examined through a standard measure, was also achieved.

Suggested Citation

  • Hashim Raza Khan & Wajahat Ahmed & Wasiq Masud & Urooj Alam & Kamran Arshad & Khaled Assaleh & Saad Ahmed Qazi, 2024. "Design and Experimental Results of an AIoT-Enabled, Energy-Efficient Ceiling Fan System," Sustainability, MDPI, vol. 16(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5047-:d:1414227
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/12/5047/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/12/5047/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Lin-Chuan & Zhou, Teng & Chang, Si-Deng & Zou, Hong-Xiang & Gao, Qiu-Hua & Wu, Zhi-Yuan & Yan, Ge & Wei, Ke-Xiang & Yeatman, Eric M. & Meng, Guang & Zhang, Wen-Ming, 2024. "A disposable cup inspired smart floor for trajectory recognition and human-interactive sensing," Applied Energy, Elsevier, vol. 357(C).
    2. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    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. Behzadi, Amirmohammad & Duwig, Christophe & Ploskic, Adnan & Holmberg, Sture & Sadrizadeh, Sasan, 2024. "Application to novel smart techniques for decarbonization of commercial building heating and cooling through optimal energy management," Applied Energy, Elsevier, vol. 376(PA).
    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.
    3. Guo, Yuxiang & Qu, Shengli & Wang, Chuang & Xing, Ziwen & Duan, Kaiwen, 2024. "Optimal dynamic thermal management for data center via soft actor-critic algorithm with dynamic control interval and combined-value state space," Applied Energy, Elsevier, vol. 373(C).
    4. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    5. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty," Applied Energy, Elsevier, vol. 371(C).
    6. Loprete, Jason & Trojanowski, Rebecca & Butcher, Thomas & Longtin, Jon & Assanis, Dimitris, 2024. "Enabling residential heating decarbonization through hydronic low-temperature thermal distribution using forced-air assistive devices," Applied Energy, Elsevier, vol. 353(PA).
    7. Rosa Francesca De Masi & Nicoletta Del Regno & Antonio Gigante & Silvia Ruggiero & Alessandro Russo & Francesco Tariello & Giuseppe Peter Vanoli, 2023. "The Importance of Investing in the Energy Refurbishment of Hospitals: Results of a Case Study in a Mediterranean Climate," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    8. Aristeidis Mystakidis & Paraskevas Koukaras & Nikolaos Tsalikidis & Dimosthenis Ioannidis & Christos Tjortjis, 2024. "Energy Forecasting: A Comprehensive Review of Techniques and Technologies," Energies, MDPI, vol. 17(7), pages 1-33, March.
    9. Yin, Cheng & Zeng, Xiangrui & Yin, Zhouping, 2024. "An improved data-driven predictive optimal control approach for designing hybrid electric vehicle energy management strategies," Applied Energy, Elsevier, vol. 375(C).
    10. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
    11. Paraskevas Koukaras & Akeem Mustapha & Aristeidis Mystakidis & Christos Tjortjis, 2024. "Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models," Energies, MDPI, vol. 17(6), pages 1-26, March.
    12. Alessandro Franco & Emanuele Crisostomi & Francesco Leccese & Antonio Mugnani & Stefano Suin, 2024. "Energy Savings in University Buildings: The Potential Role of Smart Monitoring and IoT Technologies," Sustainability, MDPI, vol. 17(1), pages 1-26, December.
    13. Adamantios Ntakaris & Gbenga Ibikunle, 2024. "Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading," Papers 2412.19372, arXiv.org, revised Dec 2024.
    14. Massimo Lauria & Maria Azzalin, 2024. "Digital Transformation in the Construction Sector: A Digital Twin for Seismic Safety in the Lifecycle of Buildings," Sustainability, MDPI, vol. 16(18), pages 1-22, September.
    15. Luo, Jielin & Shen, Yongting & Yang, Hongxing, 2024. "Investigations on an integrated air-conditioning system using technologies of desiccant dehumidification, indirect evaporative cooling and CO2 capture," Applied Energy, Elsevier, vol. 369(C).
    16. Vidal, João V. & Fonte, Tiago M.S.L. & Lopes, Luis Seabra & Bernardo, Rodrigo M.C. & Carneiro, Pedro M.R. & Pires, Diogo G. & Soares dos Santos, Marco P., 2024. "Prediction of dynamic behaviors of vibrational-powered electromagnetic generators: Synergies between analytical and artificial intelligence modelling," Applied Energy, Elsevier, vol. 376(PB).
    17. Homod, Raad Z. & Mohammed, Hayder Ibrahim & Abderrahmane, Aissa & Alawi, Omer A. & Khalaf, Osamah Ibrahim & Mahdi, Jasim M. & Guedri, Kamel & Dhaidan, Nabeel S. & Albahri, A.S. & Sadeq, Abdellatif M. , 2023. "Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent," Applied Energy, Elsevier, vol. 351(C).
    18. Nik, Vahid M. & Hosseini, Mohammad, 2023. "CIRLEM: a synergic integration of Collective Intelligence and Reinforcement learning in Energy Management for enhanced climate resilience and lightweight computation," Applied Energy, Elsevier, vol. 350(C).
    19. Sheng Hu & Gongjin Yuan & Kaifeng Hu & Cong Liu & Minghu Wu, 2023. "Non-Intrusive Load Identification Method Based on KPCA-IGWO-RF," Energies, MDPI, vol. 16(12), pages 1-14, June.
    20. Bian, Jianxiao & Wang, Jiarui & Yece, Qian, 2024. "A novel study on power consumption of an HVAC system using CatBoost and AdaBoost algorithms combined with the metaheuristic algorithms," Energy, Elsevier, vol. 302(C).

    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:jsusta:v:16:y:2024:i:12:p:5047-:d:1414227. 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.