Energy efficient design of rural prefabricated buildings based on ANN and NSGA-II DOI Creative Commons
Chaoqin Bai,

Xiaolin Xue

International Journal of Renewable Energy Development, Journal Year: 2024, Volume and Issue: 13(5), P. 995 - 1004

Published: Aug. 15, 2024

The growing concern about global climate change and the rapid development of rural areas highlight need for energy efficient building design. This study aims to establish a multi-objective optimization model based on artificial neural network (ANN) non-dominated sorting Genetic algorithm II (NSGA-II) optimize consumption prefabricated buildings. Firstly, ANN simulation technology are used build models predict consumption. Then, NSGA-II was material selection building, best scheme obtained. experimental results show that efficiency is 95%, which better than traditional method. Specifically, compared with algorithm, reduces by 16.7%, operating costs 20.0%, carbon emissions 20.0%. When cost optimization, emission difficult balance, average research design method 90% when rate low, other rates 85% rises 50%. reaches maximum, remains at 80%. These proposed robust efficient. provides comprehensive framework designing sustainable buildings can help reduce environmental impact. It has positive significance in economy new way thinking construction.

Language: Английский

Energy retrofitting of hospital buildings considering climate change: An approach integrating automated machine learning with NSGA-III for multi-objective optimization DOI
Yuan Shi, Panfeng Chen

Energy and Buildings, Journal Year: 2024, Volume and Issue: 319, P. 114571 - 114571

Published: July 20, 2024

Language: Английский

Citations

12
Integrated optimization of the building envelope and the HVAC system in office building retrofitting DOI Creative Commons
Wenjing Cui, Guiwen Liu,

Yanyan Wang

et al.

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 62, P. 105185 - 105185

Published: Sept. 21, 2024

Language: Английский

Citations

4
Genetic algorithm-based multi-objective optimisation for energy-efficient building retrofitting: A systematic review DOI Creative Commons
Konstantinos Alexakis,

Vasilis Benekis,

Panagiotis Kokkinakos

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: unknown, P. 115216 - 115216

Published: Dec. 1, 2024

Language: Английский

Citations

4
Comparative study of development scenarios to decipher carbon emissions of new/old campuses in China with urban building energy model: A case study of Southeast University DOI

Yuanhao Jiao,

Hailu Wei, Wei Wang

et al.

Building Simulation, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 3, 2024

Language: Английский

Citations

2
Energy efficient design of rural prefabricated buildings based on ANN and NSGA-II DOI Creative Commons
Chaoqin Bai,

Xiaolin Xue

International Journal of Renewable Energy Development, Journal Year: 2024, Volume and Issue: 13(5), P. 995 - 1004

Published: Aug. 15, 2024

The growing concern about global climate change and the rapid development of rural areas highlight need for energy efficient building design. This study aims to establish a multi-objective optimization model based on artificial neural network (ANN) non-dominated sorting Genetic algorithm II (NSGA-II) optimize consumption prefabricated buildings. Firstly, ANN simulation technology are used build models predict consumption. Then, NSGA-II was material selection building, best scheme obtained. experimental results show that efficiency is 95%, which better than traditional method. Specifically, compared with algorithm, reduces by 16.7%, operating costs 20.0%, carbon emissions 20.0%. When cost optimization, emission difficult balance, average research design method 90% when rate low, other rates 85% rises 50%. reaches maximum, remains at 80%. These proposed robust efficient. provides comprehensive framework designing sustainable buildings can help reduce environmental impact. It has positive significance in economy new way thinking construction.

Language: Английский

Citations

0