Enhancing building energy efficiency using a random forest model: a hybrid prediction approach
The building envelope considerably influences building energy consumption. To enhance the energy efficiency of buildings, this paper proposes an approach to predict building energy consumption based on the design of the building envelope. The design parameters of the building envelope include the co...
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sg-ntu-dr.10356-1643872023-01-18T07:46:02Z Enhancing building energy efficiency using a random forest model: a hybrid prediction approach Liu, Yang Chen, Hongyu Zhang, Limao Feng, Zongbao School of Civil and Environmental Engineering Engineering::Civil engineering Building Energy Prediction Building Envelope The building envelope considerably influences building energy consumption. To enhance the energy efficiency of buildings, this paper proposes an approach to predict building energy consumption based on the design of the building envelope. The design parameters of the building envelope include the comprehensive heat transfer coefficient and solar radiation absorption coefficient of exterior walls, comprehensive heat transfer coefficient and solar radiation absorption coefficient of the roof, comprehensive heat transfer coefficient of outer windows, and window-wall ratio. The approach is applied to optimize the design parameters of the building envelope structure of a university teaching building in northern China. First, a building information model of a teaching building is established in Revit and imported into DesignBuilder energy consumption analysis software. Subsequently, a data set of the abovementioned 6 parameters is obtained by performing orthogonal testing and energy consumption simulations. On this basis, an RF model is used to predict building energy consumption and rank the importance of each parameter, and the Pearson function is used to evaluate the corresponding correlations. The results show that the most important parameters with the highest correlations to building energy consumption are the comprehensive heat transfer coefficients of the exterior walls and outer windows and the window-wall ratio. Finally, the RF prediction results are compared to the prediction results of a BP artificial neural network (BP-ANN) and support vector machine (SVM). The findings indicate that the RF model exhibits notable advantages in building energy consumption prediction and is the optimal prediction model among the compared models. Published version This work was supported by the Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund, Project CXPY2020013, the Construction of Science and Technology Plan Project of Hubei Province (Grant No. 202041), and the National Natural Science Foundation of China (Grant No. 72031009). 2023-01-18T07:46:02Z 2023-01-18T07:46:02Z 2021 Journal Article Liu, Y., Chen, H., Zhang, L. & Feng, Z. (2021). Enhancing building energy efficiency using a random forest model: a hybrid prediction approach. Energy Reports, 7, 5003-5012. https://dx.doi.org/10.1016/j.egyr.2021.07.135 2352-4847 https://hdl.handle.net/10356/164387 10.1016/j.egyr.2021.07.135 2-s2.0-85122707978 7 5003 5012 en Energy Reports © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
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Engineering::Civil engineering Building Energy Prediction Building Envelope Liu, Yang Chen, Hongyu Zhang, Limao Feng, Zongbao Enhancing building energy efficiency using a random forest model: a hybrid prediction approach |
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The building envelope considerably influences building energy consumption. To enhance the energy efficiency of buildings, this paper proposes an approach to predict building energy consumption based on the design of the building envelope. The design parameters of the building envelope include the comprehensive heat transfer coefficient and solar radiation absorption coefficient of exterior walls, comprehensive heat transfer coefficient and solar radiation absorption coefficient of the roof, comprehensive heat transfer coefficient of outer windows, and window-wall ratio. The approach is applied to optimize the design parameters of the building envelope structure of a university teaching building in northern China. First, a building information model of a teaching building is established in Revit and imported into DesignBuilder energy consumption analysis software. Subsequently, a data set of the abovementioned 6 parameters is obtained by performing orthogonal testing and energy consumption simulations. On this basis, an RF model is used to predict building energy consumption and rank the importance of each parameter, and the Pearson function is used to evaluate the corresponding correlations. The results show that the most important parameters with the highest correlations to building energy consumption are the comprehensive heat transfer coefficients of the exterior walls and outer windows and the window-wall ratio. Finally, the RF prediction results are compared to the prediction results of a BP artificial neural network (BP-ANN) and support vector machine (SVM). The findings indicate that the RF model exhibits notable advantages in building energy consumption prediction and is the optimal prediction model among the compared models. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Liu, Yang Chen, Hongyu Zhang, Limao Feng, Zongbao |
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Article |
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Liu, Yang Chen, Hongyu Zhang, Limao Feng, Zongbao |
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Liu, Yang |
title |
Enhancing building energy efficiency using a random forest model: a hybrid prediction approach |
title_short |
Enhancing building energy efficiency using a random forest model: a hybrid prediction approach |
title_full |
Enhancing building energy efficiency using a random forest model: a hybrid prediction approach |
title_fullStr |
Enhancing building energy efficiency using a random forest model: a hybrid prediction approach |
title_full_unstemmed |
Enhancing building energy efficiency using a random forest model: a hybrid prediction approach |
title_sort |
enhancing building energy efficiency using a random forest model: a hybrid prediction approach |
publishDate |
2023 |
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https://hdl.handle.net/10356/164387 |
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1756370566870204416 |