Machine Learning Research

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Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research

Received: Apr. 29, 2023    Accepted: May 18, 2023    Published: May 29, 2023
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Abstract

A large proportion of total energy consumption is caused by buildings. Accurately predicting the heating and cooling demand of a building is crucial in the initial design phase in order to determine the most efficient solution from various designs. In this paper, in order to explore the effectiveness of basic machine learning algorithms to solve this problem, different machine learning models were used to estimate the heating and cooling loads of buildings, utilising data on the energy efficiency of buildings. Notably, this paper also discusses the performance of deep neural network prediction models and concludes that among traditional machine learning algorithms, GradientBoostingRegressor achieves better predictions, with Heating prediction reaching 0.998553 and Cooling prediction Compared with our machine learning algorithm HB-Regressor, the prediction accuracy of HB-Regressor is higher, reaching 0.998672 and 0.995153 respectively, but the fitting speed is not as fast as the GradientBoostingRegressor algorithm.

DOI 10.11648/j.mlr.20230801.11
Published in Machine Learning Research ( Volume 8, Issue 1, June 2023 )
Page(s) 1-8
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Building Energy, Decision Tree, Random Forest, Deep Learning, Gradient Descent Regression

References
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Cite This Article
  • APA Style

    Zeyu Wu, Hongyang He. (2023). Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research. Machine Learning Research, 8(1), 1-8. https://doi.org/10.11648/j.mlr.20230801.11

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    ACS Style

    Zeyu Wu; Hongyang He. Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research. Mach. Learn. Res. 2023, 8(1), 1-8. doi: 10.11648/j.mlr.20230801.11

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    AMA Style

    Zeyu Wu, Hongyang He. Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research. Mach Learn Res. 2023;8(1):1-8. doi: 10.11648/j.mlr.20230801.11

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  • @article{10.11648/j.mlr.20230801.11,
      author = {Zeyu Wu and Hongyang He},
      title = {Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research},
      journal = {Machine Learning Research},
      volume = {8},
      number = {1},
      pages = {1-8},
      doi = {10.11648/j.mlr.20230801.11},
      url = {https://doi.org/10.11648/j.mlr.20230801.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.mlr.20230801.11},
      abstract = {A large proportion of total energy consumption is caused by buildings. Accurately predicting the heating and cooling demand of a building is crucial in the initial design phase in order to determine the most efficient solution from various designs. In this paper, in order to explore the effectiveness of basic machine learning algorithms to solve this problem, different machine learning models were used to estimate the heating and cooling loads of buildings, utilising data on the energy efficiency of buildings. Notably, this paper also discusses the performance of deep neural network prediction models and concludes that among traditional machine learning algorithms, GradientBoostingRegressor achieves better predictions, with Heating prediction reaching 0.998553 and Cooling prediction Compared with our machine learning algorithm HB-Regressor, the prediction accuracy of HB-Regressor is higher, reaching 0.998672 and 0.995153 respectively, but the fitting speed is not as fast as the GradientBoostingRegressor algorithm.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research
    AU  - Zeyu Wu
    AU  - Hongyang He
    Y1  - 2023/05/29
    PY  - 2023
    N1  - https://doi.org/10.11648/j.mlr.20230801.11
    DO  - 10.11648/j.mlr.20230801.11
    T2  - Machine Learning Research
    JF  - Machine Learning Research
    JO  - Machine Learning Research
    SP  - 1
    EP  - 8
    PB  - Science Publishing Group
    SN  - 2637-5680
    UR  - https://doi.org/10.11648/j.mlr.20230801.11
    AB  - A large proportion of total energy consumption is caused by buildings. Accurately predicting the heating and cooling demand of a building is crucial in the initial design phase in order to determine the most efficient solution from various designs. In this paper, in order to explore the effectiveness of basic machine learning algorithms to solve this problem, different machine learning models were used to estimate the heating and cooling loads of buildings, utilising data on the energy efficiency of buildings. Notably, this paper also discusses the performance of deep neural network prediction models and concludes that among traditional machine learning algorithms, GradientBoostingRegressor achieves better predictions, with Heating prediction reaching 0.998553 and Cooling prediction Compared with our machine learning algorithm HB-Regressor, the prediction accuracy of HB-Regressor is higher, reaching 0.998672 and 0.995153 respectively, but the fitting speed is not as fast as the GradientBoostingRegressor algorithm.
    VL  - 8
    IS  - 1
    ER  - 

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Author Information
  • College of Engineering and Physical Sciences, The University of Birmingham, Birmingham, United Kingdom

  • College of Engineering and Physical Sciences, The University of Birmingham, Birmingham, United Kingdom

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