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Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI

Author

Listed:
  • Yafei Zhao

    (School of Architecture, Tianjin University, Tianjin300072, China)

  • Paolo Vincenzo Genovese

    (School of Architecture, Tianjin University, Tianjin300072, China)

  • Zhixing Li

    (School of Architecture, Tianjin University, Tianjin300072, China)

Abstract

With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.

Suggested Citation

  • Yafei Zhao & Paolo Vincenzo Genovese & Zhixing Li, 2020. "Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI," Future Internet, MDPI, vol. 12(2), pages 1-18, February.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:2:p:30-:d:318733
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    References listed on IDEAS

    as
    1. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    2. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    3. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
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    Cited by:

    1. V. S. K. V. Harish & Arun Kumar & Tabish Alam & Paolo Blecich, 2021. "Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    2. Davide Rolando & Willem Mazzotti Pallard & Marco Molinari, 2022. "Long-Term Evaluation of Comfort, Indoor Air Quality and Energy Performance in Buildings: The Case of the KTH Live-In Lab Testbeds," Energies, MDPI, vol. 15(14), pages 1-34, July.
    3. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    4. Ke Wang & Yafei Zhao & Rajan Kumar Gangadhari & Zhixing Li, 2021. "Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China," Sustainability, MDPI, vol. 13(19), pages 1-35, October.

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