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

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.

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

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.

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

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

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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