Relation between the Building Exterior Conditions and Energy Costs in the Running period of the Apartment Housing

공동주택의 건물외부조건과 에너지비용과의 관계분석

  • Received : 2008.11.11
  • Accepted : 2009.02.23
  • Published : 2009.02.28

Abstract

The energy cost is resulted from the energy use. Its sources are divided into some types and depended on the building use or energy-use type. The energy cost should be affected by the amount of the energy use. The cost could be calculated to consider various factors such as the insulation, heating type, building shape and others. But it can not consider all of the affect factors to the energy cost and need to categorize the factors to the condition for estimating the cost. In this paper, it aimed at providing the estimation model in linear equation and multiple linear regression, utilizing the building exterior condition and management characteristics in apartment housing. Its survey are conducted in two parts of management characteristics and building exterior condition. The correlation analysis is conducted to get rid of the multicolinearity among the inputted factors. The number of linear equation model is 11 and includes the 1st, 2nd and 3rd equation function, power function and others. Among these, it suggested the 2nd and 3rd function and power function in terms of the statistics. In multiple linear regression model, the building volume and management area are inputted to the estimation.

Keywords

References

  1. 성민기(1998), '사무소 건물에너지 소비인자의 영향력 평가방법에 관한 연구', 서울대학교 대학원 석사학위 논문, p4-7
  2. 이강희(2001), '공동주택의 유지관리비용 영향요인 분석에 관한 연구', 대한건축학회논문집 계획계 17권9호, pp321-328
  3. 이강희(2000), '공동주택 유지관리단계의 에너지 소비량 소비특성에 관한 연구', 대한건축학회논문집 계획계 16권12호, pp185-192
  4. 이강희, '공동주택 건설단계의 건축공사에 따른 에너지 소비량과 이산화탄소 배출량 산정에 관한 연구', 대한건축학회논문집 계획계 16권4호, pp125-132, 2000.4
  5. 홍성희외 3인(2002), '사무소건물의 에너지 소비원단위 설정연구', 대한건축학회논문집 계획계 18권9호 pp237-244
  6. Ralph B D'Agostino and Michael A Stephens(1986), Goodness-of-Fit Techniques, Marcel Dekker, Inc
  7. Douglas C. Montgomery and Elizabeth A. Peck(1982), Introduction to Linear Regression Analysis, John Wiley & Sons