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

Zeyu Wu; Hongyang He

doi:doi:10.11648/j.mlr.20230801.11

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2023. Published by Science Publishing Group

Keywords

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

References

[1]	Amasyali, Kadir, and Nora M. El-Gohary. "A review of data-driven building energy consumption prediction studies." Renewable and Sustainable Energy Reviews 81 (2018): 1192-1205.
[2]	Li, Chengdong, et al. "Building energy consumption prediction: An extreme deep learning approach." Energies 10.10 (2017): 1525.
[3]	Qiao, Qingyao, Akilu Yunusa-Kaltungo, and Rodger E. Edwards. "Towards developing a systematic knowledge trend for building energy consumption prediction." Journal of Building Engineering 35 (2021): 101967.
[4]	Wang, Ran, Shilei Lu, and Wei Feng. "A novel improved model for building energy consumption prediction based on model integration." Applied Energy 262 (2020): 114561.
[5]	Liu, Zhijian, et al. "Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction." Energy Exploration & Exploitation 37.4 (2019): 1426-1451.
[6]	Dong, Zhenxiang, et al. "Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification." Energy and Buildings 241 (2021): 110929.
[7]	Lei, Lei, et al. "A building energy consumption prediction model based on rough set theory and deep learning algorithms." Energy and Buildings 240 (2021): 110886.
[8]	Rigatti, Steven J. "Random forest." Journal of Insurance Medicine 47.1 (2017): 31-39.
[9]	Kotsiantis, Sotiris B. "Decision trees: a recent overview." Artificial Intelligence Review 39 (2013): 261-283.
[10]	Zhang, Yanru, and Ali Haghani. "A gradient boosting method to improve travel time prediction." Transportation Research Part C: Emerging Technologies 58 (2015): 308-324.
[11]	Nie, Peng, et al. "Prediction of home energy consumption based on gradient boosting regression tree." Energy Reports 7 (2021): 1246-1255.
[12]	He, Hongyang. "Research on the application of electronic technology of internet of things in smart city." 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2020.
[13]	Jetcheva, Jorjeta G., Mostafa Majidpour, and Wei-Peng Chen. "Neural network model ensembles for building-level electricity load forecasts." Energy and Buildings 84 (2014): 214-223.
[14]	Catalina, Tiberiu, Vlad Iordache, and Bogdan Caracaleanu. "Multiple regression model for fast prediction of the heating energy demand." Energy and buildings 57 (2013): 302-312.
[15]	Yun, Kyungtae, et al. "Building hourly thermal load prediction using an indexed ARX model." Energy and Buildings 54 (2012): 225-233.
[16]	Popescu, D., and F. Ungureanu. "Prediction of space heating consumption in district heated apartments." ASME International Mechanical Engineering Congress and Exposition. Vol. 56291. American Society of Mechanical Engineers, 2013.
[17]	Wong, Sai Li, Kevin KW Wan, and Tony NT Lam. "Artificial neural networks for energy analysis of office buildings with daylighting." Applied Energy 87.2 (2010): 551-557.
[18]	Kamaev, V. A., et al. "Using connectionist systems for electric energy consumption forecasting in shopping centers." Automation and Remote Control 73.6 (2012): 1075-1084.
[19]	Wu, Xindong, et al. "Top 10 algorithms in data mining." Knowledge and information systems 14 (2008): 1-37.
[20]	Fumo, Nelson. "A review on the basics of building energy estimation." Renewable and sustainable energy reviews 31 (2014): 53-60.
[21]	Deák, István. "Random number generators and simulation." Mathematical methods of operation research (1990).
[22]	Kwok, Simon SK, and Eric WM Lee. "A study of the importance of occupancy to building cooling load in prediction by intelligent approach." Energy Conversion and Management 52.7 (2011): 2555-2564.
[23]	Becerik-Gerber, Burcin, et al. "Civil engineering grand challenges: Opportunities for data sensing, information analysis, and knowledge discovery." Journal of Computing in Civil Engineering 28.4 (2014): 04014013.
[24]	Jain, R. K., T. Damoulas, and C. E. Kontokosta. "Towards data-driven energy consumption forecasting of multi-family residential buildings: feature selection via the lasso." Computing in Civil and Building Engineering (2014). 2014. 1675-1682.
[25]	Fan, Cheng, Fu Xiao, and Shengwei Wang. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques." Applied Energy 127 (2014): 1-10.
[26]	Chou, Jui-Sheng, and Dac-Khuong Bui. "Modeling heating and cooling loads by artificial intelligence for energy-efficient building design." Energy and Buildings 82 (2014): 437-446.
[27]	Li, Qiong, Peng Ren, and Qinglin Meng. "Prediction model of annual energy consumption of residential buildings." 2010 international conference on advances in energy engineering. IEEE, 2010.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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