Citation: Guo, J.; Li, M.; Jiang, Z.;
Wang, Z.; Zhou, Y. Optimized Design
of Floor Plan and Components of
Prefabricated Building with
Energy-Cost Effect. Appl. Sci. 2022,
12, 3740. https://doi.org/10.3390/
app12083740
Academic Editors: Edyta
Plebankiewicz and Jürgen Reichardt
Received: 14 February 2022
Accepted: 5 April 2022
Published: 8 April 2022
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Article
Optimized Design of Floor Plan and Components of
Prefabricated Building with Energy-Cost Effect
Juanli Guo
1
, Mingchen Li
2,
*, Zixin Jiang
2
, Zhoupeng Wang
2
and Yangkong Zhou
2
1
School of Architecture, Tianjin University, Tianjin 300072, China; guojuanli@tju.edu.cn
2
Tianjin International Engineering Institute, Tianjin University, Tianjin 300072, China; jiangzx@tju.edu.cn (Z.J.);
wangzhoupeng@tju.edu.cn (Z.W.); zyk36@tju.edu.cn (Y.Z.)
* Correspondence: limingchen@tju.edu.cn; Tel.: +86-13059077720
Abstract:
Optimizing building performance and economic benefits through feedback in building
design is a hot topic in current academic research. However, few studies on prefabricated buildings
have been undertaken in this field. Meanwhile, the methodology used for achieving optimized
solutions is still poor. In this paper, genetic algorithms and correlation analysis are employed
and two parametric design methods—i.e., the floor plan generation method and the component
selection method—are proposed for the modularity of the prefabricated buildings. Taking a typical
high-rise building in Tianjin as an example, correlation analyses are performed on the basis of the
two proposed methods to enhance the depth of the optimized finding approach. The outcome of
this research demonstrates the feasibility of the proposed numerical approach, which can produce
the optimized floor plan and construction set under the local conditions. This also reveals that the
shape coefficient and window-to-wall ratio are strongly correlated with the energy performance
of a building, which can help architects to pursue optimized design solutions in the schematic
design process.
Keywords:
building energy saving; prefabricated building; genetic algorithm; parametric design;
multi-objective optimization; correlation analysis
1. Introduction
The issue of global energy consumption is in the spotlight today. The U.S. Information
Administration presents a future scenario in which global energy consumption will increase
by nearly 50% over the next 30 years or so [
1
]. Building energy consumption accounts for
21.7% of the national energy consumption in China [
2
], and there is still work to be done to
reduce overall energy consumption in the building industry.
Cost limits, of course, are crucial factors in restricting building energy efficiency [
3
].
The optimization of energy efficiency without the consideration of cost may result in high
incremental costs and the inability to promote applications. As a result, energy usage and
cost should be considered throughout the optimization process.
Many earlier researchers have investigated this topic using the genetic algorithm
technique. I2n 2002, Caldas et al. used evolutionary algorithms on the DOE2.1E platform
to improve the arrangement and size of windows in public buildings to lower the yearly
energy consumption [
4
]. Ferrara et al. optimized near-zero-energy buildings to achieve
a low economic cost [
5
]. Thalfeldt et al. identified the design priorities for cold-climate
building facades [
6
]. However, related research has revealed that diverse optimization
targets have conflicting characteristics [
7
], suggesting that focusing on a single goal is
unsuitable in particular optimization schemes. As a result, an increasing number of
researchers are attempting to adapt the multi-objective optimum design technique to the
purpose of the architecture. With illumination and energy consumption as the optimization
targets, Khoroshiltseva et al. employed modeFRONTIER and Daysim to optimize the
Appl. Sci. 2022, 12, 3740. https://doi.org/10.3390/app12083740 https://www.mdpi.com/journal/applsci
