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

Author

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  • 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
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    References listed on IDEAS

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    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).
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