为未来设计小型温室:能源效率和适应性评估

Q2 Energy
Lisheng Chen
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引用次数: 0

摘要

随着全球建筑能耗占比接近40%,加之人口增长和城市化进程加快,住房需求急剧上升,小型绿色住宅作为可持续发展的关键模式备受关注。本研究着眼于未来小型绿色住宅的设计,旨在对其能源效率和适应性进行综合评价。结合热力学、传热学、工效学等多学科理论,构建创新的综合评价模型SGH-EAM,整合能效评价组件、适应性评价组件和融合决策组件。在100个不同地区的小型绿色住宅案例中进行了实验,并与EEM-GH和SA-HM等模型进行了比较。结果表明,SGH-EAM模型在节能方面表现良好,年平均供暖能耗降低30%,制冷能耗降低25%,照明能耗降低35%。适应性方面,空间可调灵活性综合得分为80分。综合评价得分为83分,显著高于其他模型。研究表明,SGH-EAM模型可以有效提高小型绿色住宅评价的准确性,为其设计提供全面的理论依据,促进住宅建设朝着绿色、智能化、可持续的方向发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing small green houses for the future: energy efficiency and adaptability assessment

With the global building energy consumption accounting for nearly 40% and the housing demand rising sharply due to population growth and accelerated urbanization, small green housing has attracted much attention as a key model for sustainable development. This study focuses on the design of small green housing for the future, aiming to comprehensively evaluate its energy efficiency and adaptability. By constructing an innovative comprehensive evaluation model SGH-EAM, integrating energy efficiency evaluation components, adaptability evaluation components and fusion decision components, the model is derived using multidisciplinary theories such as thermodynamics, heat transfer, and ergonomics. Experiments were conducted on small green housing cases in 100 different regions, and compared with models such as EEM-GH and SA-HM. The results show that the SGH-EAM model performs well in energy efficiency, with an average annual heating energy consumption reduction rate of 30%, cooling energy consumption reduction of 25%, and lighting energy consumption reduction of 35%. In terms of adaptability, the spatial adjustable flexibility has a comprehensive score of 80 points. The comprehensive evaluation score is 83 points, which is significantly higher than other models. Research shows that the SGH-EAM model can effectively improve the accuracy of small green housing assessment, provide a comprehensive theoretical basis for its design, and promote the development of housing construction towards a green, intelligent and sustainable direction.

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来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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