Multi-Objective Optimization of Envelope Structures for Rural Dwellings in Qianbei Region, China: Synergistic Enhancement of Energy Efficiency, Thermal Comfort, and Economic Viability

In China, retrofitting rural dwellings is a crucial step toward enhancing living conditions and lowering energy waste. One of the most important ways to enhance building performance is to retrofit the building envelope. The Qianbei Region’s (Northern Guizhou Province, China) rural dwellings are the...

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Vydané v:Buildings (Basel) Ročník 15; číslo 8; s. 1367
Hlavní autori: Chu, Yan, Li, Junjun, Zhao, Pengfei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.04.2025
Predmet:
Algorithms
Analysis
Architecture and energy conservation
building envelope
Building envelopes
Carbon
China
Cost control
Design optimization
Discomfort
DNA nucleotidylexotransferase
Dwellings
Economic conditions
Economic development
Economics
Energy conservation
Energy consumption
Energy efficiency
Energy management systems
Energy use
Genetic algorithms
Green buildings
Heating
Indoor air quality
Indoor environments
Insulation
Life cycle costs
Living conditions
multi-objective optimization
Multiple objective analysis
NSGA-II algorithm
Optimization
Optimization algorithms
Optimization techniques
Per capita
Quality of life
Retrofitting
Rural areas
rural dwellings
Simulation
Sorting algorithms
Surveys
Thermal comfort
Thermal environments
Ventilation
China
ISSN:2075-5309, 2075-5309
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Shrnutí:In China, retrofitting rural dwellings is a crucial step toward enhancing living conditions and lowering energy waste. One of the most important ways to enhance building performance is to retrofit the building envelope. The Qianbei Region’s (Northern Guizhou Province, China) rural dwellings are the subject of this study. It identifies the persistent issue of inadequate thermal comfort in local rural dwellings through indoor thermal environment measurements and questionnaire surveys. Using a parametric modelling tool (Rhino-Grasshopper-Ladybug Tools), multi-objective optimization was performed using a non-dominated sorting genetic algorithm (NSGA-II), with the types of external windows, walls, and roof insulation as optimization variables, and building energy consumption (E), annual thermal discomfort hours (TDT), and life cycle cost increment (ΔLCC) as optimization objectives. After the retrofitting, the building’s energy consumption was reduced from the baseline value of 96.41 kWh/m2 to 42.40 kWh/m2 (a 56% reduction), and the annual duration of thermal discomfort decreased from 6173 h to 5078 h (a 17.7% decrease). This resulted in a positive economic return, with a cost saving of ΔLCC = −56,329.87 CNY. The research proposes a scientific method for the energy-saving retrofitting of rural dwellings in the Qianbei Region, which also serves as a guide for the optimization of building performance in comparable climate zones.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings15081367