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Prediction of Cooling Load of Tropical Buildings with Machine Learning

Author

Listed:
  • Gebrail Bekdaş

    (Department of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, Turkey)

  • Yaren Aydın

    (Department of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, Turkey)

  • Ümit Isıkdağ

    (Department of Informatics, Mimar Sinan Fine Arts University, Istanbul 34427, Turkey)

  • Aidin Nobahar Sadeghifam

    (Department of Civil Engineering, Curtin University Malaysia, Miri 98009, Malaysia)

  • Sanghun Kim

    (Department of Civil and Environmental Engineering, Temple University, Philadelphia, PA 19122, USA)

  • Zong Woo Geem

    (Department of Smart City, Gachon University, Seongnam 13120, Republic of Korea)

Abstract

Cooling load refers to the amount of energy to be removed from a space (or consumed) to bring that space to an acceptable temperature or to maintain the temperature of a space at an acceptable range. The study aimed to develop a series of models and determine the most accurate ones in the prediction of the cooling load of low-rise tropical buildings based on their basic architectural and structural characteristics. In this context, a series of machine learning (regression) algorithms were tested during the research to determine the most accurate/efficient prediction model. In this regard, a data set consisting of ten features indicating the basic characteristics of the building (floor area, aspect ratio, ceiling height, window material, external wall material, roof material, window wall ratio north faced, window wall ratio south faced, horizontal shading, orientation) were used to predict the cooling load of a low-rise tropical building. The dataset was generated utilizing a set of generative and algorithmic design tools. Following the dataset generation, a series of regression models were tested to find the most accurate model to predict the cooling load. The results of the tests with different algorithms revealed that the relationship between the predictor variables and cooling load could be efficiently modeled through Histogram Gradient Boosting and Stacking models.

Suggested Citation

  • Gebrail Bekdaş & Yaren Aydın & Ümit Isıkdağ & Aidin Nobahar Sadeghifam & Sanghun Kim & Zong Woo Geem, 2023. "Prediction of Cooling Load of Tropical Buildings with Machine Learning," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:9061-:d:1163328
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    References listed on IDEAS

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    1. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
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    Cited by:

    1. Hyungah Lee & Woojin Cho & Jong-hyeok Park & Jae-hoi Gu, 2024. "Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning," Energies, MDPI, vol. 17(10), pages 1-16, May.
    2. Jianwu Xiong & Linlin Chen & Yin Zhang, 2023. "Building Energy Saving for Indoor Cooling and Heating: Mechanism and Comparison on Temperature Difference," Sustainability, MDPI, vol. 15(14), pages 1-20, July.

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