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Evaluation of energy retrofits for existing office buildings through uncertainty and sensitivity Analyses: A case study of Tianjin, China
Energy and Buildings ( IF 6.6 ) Pub Date : 2025-01-24 , DOI: 10.1016/j.enbuild.2025.115363
Wenyuan Wang, Li Zhu, Jiqiang Zhang, Xingzhe Zhu, Zhexing Yan, Mengying Cao

In the practice of energy retrofitting for existing buildings, uncertainty in retrofit parameters significantly impacts the outcomes. To support planning decisions for energy retrofits, a Monte Carlo (MC) method combining Latin hypercube sampling (LHS) is proposed to evaluate the uncertainty analysis (UA) of four building energy retrofit schemes. Furthermore, the important factors affecting outputs are examined using two global sensitivity analysis (GSA) methods, including meta modeling method based on treed Gaussian process (TGP) and regression method based on standard regression coefficient (SRC). Finally, a case study of office building is conducted. The results demonstrate that the UA can quantitatively assess the distribution of energy-savings at the primary stage. The energy savings rates for the four typical retrofit scenarios fluctuated between 16.5% and 27.9%, 27.4%-42.5%, 32.7%-55.2%, and 55.5%-108.4%, with the highest energy savings potential also being the most uncertain. The retrofit success probabilities of Passive Retrofit Measures(PRM) and Active Retrofit Measures(ARM) are 88.8% and 100%, respectively, indicating greater success probabilities with proactive measures. Active-Passive Retrofit Measures (APRM) and Active-Passive and Renewable Energy Retrofit Measures(APRERM) had success probabilities of 58% and 96.4%, respectively, with renewable energy technologies ensuring retrofit results. Renewable energy technologies alter the distribution of energy-saving potential. SA indicates that in passive technology retrofits, the infiltration rate and wall insulation thickness significantly impact energy consumption, whereas in active technology retrofits, the efficiency of heating and cooling systems is highly influential. This study provides valuable insights for evaluating the feasibility and robustness of energy-saving retrofit schemes for existing buildings.

中文翻译:


基于不确定性和敏感性的既有办公楼能源改造评价分析——以天津为例



在现有建筑物的能源改造实践中,改造参数的不确定性会显著影响结果。为了支持能源改造的规划决策,提出了一种结合拉丁超立方体采样 (LHS) 的蒙特卡洛 (MC) 方法来评估四种建筑能源改造方案的不确定性分析 (UA)。此外,使用两种全局敏感性分析 (GSA) 方法检查影响输出的重要因素,包括基于三高斯过程 (TGP) 的元建模方法和基于标准回归系数 (SRC) 的回归方法。最后,对办公楼进行了案例研究。结果表明,UA 可以定量评估初级阶段节能的分布。四种典型改造方案的节能率在 16.5% 和 27.9%、27.4%-42.5%、32.7%-55.2% 和 55.5%-108.4% 之间波动,其中最高的节能潜力也是最不确定的。被动改造措施 (PRM) 和主动改造措施 (ARM) 的改造成功概率分别为 88.8% 和 100%,表明主动措施的成功概率更高。主动-被动改造措施 (APRM) 和主动-被动和可再生能源改造措施 (APRERM) 的成功概率分别为 58% 和 96.4%,可再生能源技术确保了改造结果。可再生能源技术改变了节能潜力的分布。SA 表明,在被动技术改造中,渗透率和墙体保温厚度会显着影响能源消耗,而在主动技术改造中,加热和冷却系统的效率具有很大影响。 本研究为评估现有建筑节能改造方案的可行性和稳健性提供了有价值的见解。
更新日期:2025-01-24
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