Optimizing energy efficiency and indoor thermal comfort in rural self-built housing: A comparative study of GA and EA algorithms
With the growing global focus on sustainable building design, reducing energy consumption and carbon emissions while improving indoor thermal comfort has become a critical research goal. This study investigates how building design parameters influence electricity consumption, CO2 emissions, and indo...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25009657 |
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| Summary: | With the growing global focus on sustainable building design, reducing energy consumption and carbon emissions while improving indoor thermal comfort has become a critical research goal. This study investigates how building design parameters influence electricity consumption, CO2 emissions, and indoor thermal comfort, and compares the performance of Genetic Algorithm (GA) and Evolutionary Algorithm (EA) in optimizing these objectives. A private rural residence in Huzhou, Zhejiang Province, was selected as the case study, and actual field data—including wall-to-window ratio, set-point temperatures, roof shading, equipment power density, airtightness, and envelope thermal performance—were used to build a detailed simulation model in DesignBuilder. Both GA and EA were applied under identical settings to generate Pareto-optimal solutions for three objective pairs. The results show that GA achieves better convergence speed, stability, and trade-off quality than EA, reducing electricity use by 4.28 %, indoor thermal discomfort time by 31.56 %, and CO2 emissions by 3.39 %. Sensitivity analysis further reveals that GA provides more focused responses on key variables such as heating set-point and equipment load, whereas EA exhibits broader solution diversity but less consistency. This study contributes by providing the first systematic comparison of GA and EA in the context of rural self-built housing, grounding optimization parameters in real-world measurements, and proposing a practical multi-objective strategy to support low-carbon, comfort-oriented retrofitting in rural buildings. |
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| ISSN: | 2214-157X |