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Optimizing the insulation thickness of walls and roofs of existing buildings based on primary energy consumption, global cost and pollutant emissions

机译:根据一次能源消耗,全球成本和污染物排放量,优化现有建筑物的墙壁和屋顶的隔热厚度

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An optimization model used to determine the optimum insulation thickness (OIT) of walls and roofs of existing buildings was established. Primary energy saving ratio (PESR), global cost saving ratio (GCSR) and pollutant emission reduction ratio (PERK) were used as evaluation criteria. Least square method was used to fit the polynomials which indicated the relationship between annual air-conditioning demand and insulation thickness. An existing building was used as the basis of the case study. The average relative error on annual heating and cooling demand was 0.23% and 0.035%, respectively, which indicated the accuracy of both fitting polynomials. Four types of heat and cold sources were considered to be used for the case building, respectively. Sensitivity analysis was carried out to investigate the impact of economic and energy factors on the optimization results. Results showed that the OIT of walls and roofs could be obtained by using the optimization model. The OIT of walls and roofs, building performance indicators and overall evaluation criterion during building envelope retrofit depended on the weight coefficients of evaluation criteria and types of heat and cold sources. The impact of sensitivity factors on the optimization results for the four types of heat and cold sources were different. (C) 2018 Elsevier Ltd. All rights reserved.
机译:建立了用于确定现有建筑物的墙壁和屋顶的最佳隔热厚度(OIT)的优化模型。评估标准为一次能源节约率(PESR),全球成本节约率(GCSR)和污染物减排率(PERK)。最小二乘方法用于拟合多项式,该多项式表明了年度空调需求与保温层厚度之间的关系。现有建筑物用作案例研究的基础。年供暖和制冷需求的平均相对误差分别为0.23%和0.035%,这表明两个拟合多项式的准确性。考虑将四种热源和冷源分别用于案例构建。进行了敏感性分析,以调查经济和能源因素对优化结果的影响。结果表明,通过优化模型可以得到墙体和屋顶的OIT。建筑物围护结构改造期间墙壁和屋顶的OIT,建筑性能指标和总体评估标准取决于评估标准的权重系数以及冷热源的类型。敏感性因素对四种冷热源优化结果的影响是不同的。 (C)2018 Elsevier Ltd.保留所有权利。

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