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

Zeyu Wu; Hongyang He

doi:doi:10.11648/j.mlr.20230801.11

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

Zeyu Wu, Hongyang He

Published in Machine Learning Research (Volume 8, Issue 1)

Received: 29 April 2023 Accepted: 18 May 2023 Published: 29 May 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.

Published in	Machine Learning Research (Volume 8, Issue 1)
DOI	10.11648/j.mlr.20230801.11
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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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://article.sciencepublishinggroup.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

Zeyu Wu

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

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

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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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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  -

Copy | Download