Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164387
Title: Enhancing building energy efficiency using a random forest model: a hybrid prediction approach
Authors: Liu, Yang
Chen, Hongyu
Zhang, Limao
Feng, Zongbao
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: 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
Journal: Energy Reports
Abstract: 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.
URI: https://hdl.handle.net/10356/164387
ISSN: 2352-4847
DOI: 10.1016/j.egyr.2021.07.135
Rights: © 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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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