<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the context of the digital development of big data and information in recent years, performance-based residential building design and its optimization methods have gradually become a research hotspot of domestic and international attention. The simulated annealing algorithm is a major component of residential buildings’ optimal energy efficiency design. Although the crossover operator under the computational traditional simulated annealing algorithm incorporates the respective advantages of the genetic algorithm and simulated annealing algorithm, the purely optimal genetic simulated annealing algorithm is more effective for the evaluation of index analysis and better reflects the superiority of the algorithm. In the Energy Plus model, the three algorithms were used to analyze the design indexes of ecological energy-efficient residential buildings, and the SS values of the MOSA algorithm fluctuated between 10% and 24%, and the performance of the recommended genetic simulated annealing algorithm was significantly better than the other two algorithms. This study effectively solves the difficulties of low efficiency and high failure rate common in design practice when using this technique and is of historical importance to the development of energy-efficient residential buildings in China.</p>
</abstract>

In the context of the digital development of big data and information in recent years, performance-based residential building design and its optimization methods have gradually become a research hotspot of domestic and international attention. The simulated annealing algorithm is a major component of residential buildings’ optimal energy efficiency design. Although the crossover operator under the computational traditional simulated annealing algorithm incorporates the respective advantages of the genetic algorithm and simulated annealing algorithm, the purely optimal genetic simulated annealing algorithm is more effective for the evaluation of index analysis and better reflects the superiority of the algorithm. In the Energy Plus model, the three algorithms were used to analyze the design indexes of ecological energy-efficient residential buildings, and the SS values of the MOSA algorithm fluctuated between 10% and 24%, and the performance of the recommended genetic simulated annealing algorithm was significantly better than the other two algorithms. This study effectively solves the difficulties of low efficiency and high failure rate common in design practice when using this technique and is of historical importance to the development of energy-efficient residential buildings in China.

Exploring the design of ecological and energy-saving residential buildings adapted to climate change based on a simulated annealing algorithm

The Tourism College of Changchun University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of the digital development of big data and information in recent years, performance-based residential building design and its optimization...

Exploring the design of ecological and energy-saving residential buildings adapted to climate change based on a simulated annealing algorithm

Online veröffentlicht: 24. Juli 2023

Seitenbereich: -

Eingereicht: 24. März 2022

Akzeptiert: 19. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00074

Schlüsselwörter
Big data, Simulated annealing algorithm, Crossover operator, Energy Plus model, Residential building design

© 2023 Hui Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the design of ecological and energy-saving residential buildings adapted to climate change based on a simulated annealing algorithm

Online veröffentlicht: 24. Juli 2023

Seitenbereich: -

Eingereicht: 24. März 2022

Akzeptiert: 19. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00074

SchlüsselwörterBig data, Simulated annealing algorithm, Crossover operator, Energy Plus model, Residential building design

© 2023 Hui Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data, Simulated annealing algorithm, Crossover operator, Energy Plus model, Residential building design