DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Chang, Seong Ju | - |
dc.contributor.advisor | 장성주 | - |
dc.contributor.author | Son, Jun Tae | - |
dc.date.accessioned | 2018-06-20T06:12:51Z | - |
dc.date.available | 2018-06-20T06:12:51Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=718522&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/242692 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 건설및환경공학과, 2017.8,[viii, 89 p. :] | - |
dc.description.abstract | Humans rely heavily on fossil fuels to product energy for long-term, and many studies predict that the world's coal and oil reserves will be depleted significantly between 35 and 60 years. Approximately 40 percent of the total US energy consumption is used to sustain buildings, most of which are derived from traditional energy sources such as fossil fuels. In addition, using these traditional energy sources will cause greenhouse gas emissions and face the major challenges of global climate change. Therefore, in many fields, energy consumption is analyzed using various simulation programs in order to reduce the energy consumption in buildings. However, it is difficult to simulate and analyze every time changing location, characteristics, and materials of buildings. First, the existing data was provided by U.S. Energy Information Administration and analyzed. Second, this study used principal component analysis to determine which of the more than 400 building variables in the data have the greatest effect on fuel and electricity energy. Third, this study analyzed the building variables using an online analysis processing method capable of multidimensional analysis by the various levels of abstraction. It is possible to analyze the building variables extracted from the principal component analysis in various perspectives. Fourth, various rules were extracted using association analysis method based on the results of on-line analytical processing method. Using this method, this study was able to extract the rules for combinations of building variables, not for one building variable. Therefore, this study is to analyze by using soft computing methods based on the collected building data in the US, and to examine what building variables greatly affect building energy consumption. Simulation using these analyzed results will save a lot of time and cost, which will be a way to reduce future building energy consumption and further protect the environment. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Building Energy Use▼aClimate Change▼aData Mining▼aOn-Line Analytical Processing▼aPrincipal Component Analysis▼aAssociation Rule Mining | - |
dc.subject | 건축물 에너지 사용▼a기후변화▼a데이터 마이닝▼a온라인 분석 처리▼a연관분석 | - |
dc.title | Impact of building characteristics on the patterns of building energy consumption in different climate conditions | - |
dc.title.alternative | 기후지역 별 건축적 특성이 건축물 에너지 소비패턴에 미치는 영향에 관한 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :건설및환경공학과, | - |
dc.contributor.alternativeauthor | 손준태 | - |
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