Optimizing Building Performance for Energy Efficiency and Occupant Comfort

Strategic Insights -

Research conducted by the University of Birmingham has found that buildings contribute to over 30% of global energy consumption. By optimizing building performance, individuals can reduce energy consumption, improve occupant comfort, and enhance well-being. The study utilizes a data-driven multi-objective optimization framework that allows for flexible space usage, leveraging surrogate machine learning models for room-level energy prediction and Multi-objective Evolutionary Algorithms (MOEAs) enhanced with a wall-reordering mutation strategy to balance energy usage with thermal feelings of occupants. The framework was validated through two case studies, demonstrating a 10% reduction in energy consumption while improving occupant comfort.

Key Takeaways:

  • Buildings are responsible for over 30% of global energy consumption, highlighting the potential for optimization to reduce energy consumption and improve occupant comfort.
  • A data-driven multi-objective optimization framework has been proposed to optimize building performance, leveraging surrogate machine learning models for room-level energy prediction and MOEAs with a wall-reordering mutation strategy.
  • The framework has been validated through two case studies, demonstrating a 10% reduction in energy consumption while improving occupant comfort.
  • The research emphasizes the importance of flexible building space usage in achieving sustainability and occupant well-being goals.
  • Authors Shuo Wang, Huanbo Lyu, and Shiqiao Zhou contributed to the research, alongside other experts in the field of information technology.
  • The study's findings have the potential to transform the way buildings are designed and operated, with significant implications for energy efficiency and occupant comfort.

Statistics:

  • 30%: The percentage of global energy consumption attributed to buildings.
  • 10%: The reduction in energy consumption achieved through the data-driven multi-objective optimization framework.
  • 2: The number of case studies used to validate the framework's effectiveness.
  • 346: The reference number of the Energy and Buildings journal supplement where the research was published.

Sources:

  • Wang, Shuo, et al. "A Data-driven Multi-objective Optimisation Framework for Energy Efficiency and Thermal Comfort In Flexible Building Spaces." Energy and Buildings, vol. 346, 2025, p. 346.
  • NewsRx. "Researchers from University of Birmingham Provide Details of New Studies and Findings in the Area of Information Technology (A Data-driven Multi-objective Optimisation Framework for Energy Efficiency and Thermal Comfort In Flexible Building ...)." Information Technology Newsweekly, November 4, 2025, p. 779.