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Development of a decision support model for determining the target multi-family housing complex for green remodeling using data mining techniques

机译:开发决策支持模型,以确定使用数据挖掘技术进行绿色改造的目标多户住宅建筑群

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To achieve the national CO2 emission reduction target in the building sector, green remodeling of deteriorated buildings with a low building energy efficiency level should be carried out. There is no reasonable decision support model for green remodeling, however, that is capable of determining the target building, which has low energy performance, from the viewpoint of the non-expert building owners and policy-makers. To solve this problem, this study developed a decision support model for determining the target multi-family housing complex (MFHC) for green remodeling using data mining techniques. Specifically, a total of 589 MFHCs were classified into three groups using a decision tree. Based on the operational rating system, the CO2 emission (CE) intensity by group was analyzed, and the results showed that Case No. 1,089 (0.0368 tCO(2)/m(2)) was the target MFHC where green modeling needed to be performed first. Also, most of the 88 MFHCs belong to grade E, the lowest grade in terms of CE intensity, were located in specific regions (i.e., Gangnam-gu, Seocho-gu, etc.). Thus, the developed decision support model can be used to determine the regions with a high demand for green remodeling, and to establish an efficient government budget. (c) 2019 Elsevier B.V. All rights reserved.
机译:为了实现国家在建筑领域减少二氧化碳排放的目标,应该对建筑物的低能效水平的建筑物进行绿色改造。从非专业建筑业主和政策制定者的角度来看,尚无合理的绿色改造决策支持模型能够确定目标建筑,而该建筑的能源效率低。为了解决这个问题,本研究开发了一种决策支持模型,用于确定使用数据挖掘技术进行绿色改造的目标多户住宅建筑群(MFHC)。具体而言,使用决策树将总共589种MFHC分为三类。基于运营评级系统,按组分析了CO2排放(CE)强度,结果表明,案例1,089(0.0368 tCO(2)/ m(2))是需要进行绿色建模的目标MFHC首先执行。另外,88种MFHC中的大多数属于E级,即CE强度最低的级别,位于特定区域(即江南区,瑞草区等)。因此,开发的决策支持模型可用于确定对绿色改造有很高需求的地区,并建立有效的政府预算。 (c)2019 Elsevier B.V.保留所有权利。

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