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Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

Author

Listed:
  • Hidayatus Sibyan

    (Department of Informatics Engineering, Qur’anic Science University (Universitas Sains Al-Qur’an), Jl. Hasyim Asy’ari Km. 03, Wonosobo 56351, Indonesia)

  • Jozef Svajlenka

    (Department of Construction Technology and Management, Technical University of Košice, 042 00 Košice, Slovakia)

  • Hermawan Hermawan

    (Department of Architecture, Qur’anic Science University (Universitas Sains Al-Qur’an), Jl. Hasyim Asy’ari Km. 03, Wonosobo 56351, Indonesia)

  • Nasyiin Faqih

    (Department of Civil Engineering, Qur’anic Science University (Universitas Sains Al-Qur’an), Jl. Hasyim Asy’ari Km. 03, Wonosobo 56351, Indonesia)

  • Annisa Nabila Arrizqi

    (Department of Civil Engineering, Universitas Islam Indonesia, Jl. Kaliurang Km.14,5, Yogyakarta 55584, Indonesia)

Abstract

Various data analysis methods can make thermal comfort prediction models. One method that is often used is multiple linear regression statistical analysis. Regression analysis needs to be checked for accuracy with other analytical methods. This study compares the making of a thermal comfort prediction model with regression analysis and naïve Bayes analysis. The research method used quantitative methods for data collection regarding thermal comfort. The thermal comfort variable, consisting of eight independent variables and one dependent variable, was measured at Wonosobo High School, Indonesia. The analysis to make the prediction model was carried out with two different analyses: multiple linear regression analysis and naïve Bayes analysis. The results show that naïve Bayes is more accurate than multiple linear regression analysis.

Suggested Citation

  • Hidayatus Sibyan & Jozef Svajlenka & Hermawan Hermawan & Nasyiin Faqih & Annisa Nabila Arrizqi, 2022. "Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15663-:d:983341
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    References listed on IDEAS

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    1. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    2. Heidari, Amirreza & Maréchal, François & Khovalyg, Dolaana, 2022. "Reinforcement Learning for proactive operation of residential energy systems by learning stochastic occupant behavior and fluctuating solar energy: Balancing comfort, hygiene and energy use," Applied Energy, Elsevier, vol. 318(C).
    3. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Elnour, Mariam & Himeur, Yassine & Fadli, Fodil & Mohammedsherif, Hamdi & Meskin, Nader & Ahmad, Ahmad M. & Petri, Ioan & Rezgui, Yacine & Hodorog, Andrei, 2022. "Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities," Applied Energy, Elsevier, vol. 318(C).
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