Design optimization considering variable thermal mass, insulation, absorptance of solar radiation, and glazing ratio using a prediction model and genetic algorithm

Lin, Y, Zhou, S, Yang, W and Liu, C 2018, 'Design optimization considering variable thermal mass, insulation, absorptance of solar radiation, and glazing ratio using a prediction model and genetic algorithm', Sustainability, vol. 10, no. 2, pp. 1-15.


Document type: Journal Article
Collection: Journal Articles

Title Design optimization considering variable thermal mass, insulation, absorptance of solar radiation, and glazing ratio using a prediction model and genetic algorithm
Author(s) Lin, Y
Zhou, S
Yang, W
Liu, C
Year 2018
Journal name Sustainability
Volume number 10
Issue number 2
Start page 1
End page 15
Total pages 15
Publisher MDPI AG
Abstract This paper presents the optimization of building envelope design to minimize thermal load and improve thermal comfort for a two-star green building inWuhan, China. The thermal load of the building before optimization is 36% lower than a typical energy-efficient building of the same size. A total of 19 continuous design variables, including different concrete thicknesses, insulation thicknesses, absorbance of solar radiation for each exterior wall/roof and different window-to-wall ratios for each façade, are considered for optimization. The thermal load and annual discomfort degree hours are selected as the objective functions for optimization. Two prediction models, multi-linear regression (MLR) model and an artificial neural network (ANN) model, are developed to predict the building thermal performance and adopted as fitness functions for a multi-objective genetic algorithm (GA) to find the optimal design solutions. As compared to the original design, the optimal design generated by the MLRGA approach helps to reduce the thermal load and discomfort level by 18.2% and 22.4%, while the reductions are 17.0% and 22.2% respectively, using the ANNGA approach. Finally, four objective functions using cooling load, heating load, summer discomfort degree hours, and winter discomfort degree hours for optimization are conducted, but the results are no better than the two-objective-function optimization approach.
Subject Building Construction Management and Project Planning
Keyword(s) Design optimization
Prediction model
Thermal comfort
Thermal load
DOI - identifier 10.3390/su10020336
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. Open access, (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
ISSN 2071-1050
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