Optimizing Energy Efficiency and Indoor Thermal Comfort in Rural Self-Built Housing

Strategic Insights -

Research has been conducted to explore the most effective methods for optimizing energy efficiency and indoor thermal comfort in rural self-built housing. With the growing 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. The research found 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%.

Key Takeaways:

  • The study investigated the impact of building design parameters on energy consumption, CO2 emissions, and indoor thermal comfort in rural self-built housing.
  • Genetic Algorithm (GA) was found to be more effective than Evolutionary Algorithm (EA) in optimizing energy efficiency and indoor thermal comfort, achieving better convergence speed, stability, and trade-off quality.
  • The results showed that GA reduced electricity use by 4.28%, indoor thermal discomfort time by 31.56%, and CO2 emissions by 3.39%.
  • Sensitivity analysis revealed that GA provided more focused responses on key variables such as heating set-point and equipment load, whereas EA exhibited broader solution diversity but less consistency.
  • The research proposed a practical multi-objective strategy to support low-carbon, comfort-oriented retrofitting in rural buildings.
  • The study used a real-world case study of a private rural residence in Huzhou, Zhejiang Province, China, and actual field data were used to build a detailed simulation model in DesignBuilder.
  • The research found that GA and EA were both effective in optimizing energy efficiency and indoor thermal comfort, but GA provided more focused results.

Statistics:

  • GA reduced electricity use by 4.28%.
  • GA reduced indoor thermal discomfort time by 31.56%.
  • GA reduced CO2 emissions by 3.39%.
  • GA achieved better convergence speed, stability, and trade-off quality than EA.
  • EA exhibited broader solution diversity but less consistency than GA.

Sources:

  • "Optimizing Energy Efficiency and Indoor Thermal Comfort In Rural Self-built Housing: a Comparative Study of Ga and Ea Algorithms." Case Studies in Thermal Engineering, 2025;73. Citation: NewsRx. Studies from Jiaxing University Further Understanding of Thermal Engineering (Optimizing Energy Efficiency and Indoor Thermal Comfort In Rural Self-built Housing: a Comparative Study of Ga and Ea Algorithms). Global Warming Focus. September 1, 2025; p 3918.
  • Jiaxing University, College of Civil Engineering & Architecture, Jiaxing 314001, People's Republic of China.