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<p>With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5&#xa0;kW&#xa0;h/m<sup>2</sup>, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies.</p>

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Performance optimization of high-rise residential buildings in cold regions considering energy consumption

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月27日

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  • Liwei Song

摘要

With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5 kW h/m2, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies.