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2025 Vol.19, Issue 5 Preview Page

Research Article

Causal Analysis to Building Vintage and Heating/Cooling Energy Consumption in Korean Office Buildings

국내 사무소 건물의 준공연도와 냉난방 에너지 사용량에 대한 인과적 해석

Young-Seo Yoo1
,
Cheol-Soo Park2
,
Deuk-Woo Kim3
,
Ki-Uhn Ahn4*
유 영서1
,
박 철수2
,
김 덕우3
,
안 기언4*
1Post-doctoral Researcher, Department of Building Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
2Professor, Department of Architecture and Architectural Engineering, Seoul National University, Seoul, Korea
3Senior Researcher, Department of Building Energy Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
4Senior Researcher, Department of Building Research, Korea Institute of Civil Engineering and Building Technology, Goyang, Korea
1한국건설기술연구원 건축연구본부 박사후연구원
2서울대학교 건축학과 교수
3한국건설기술연구원 건축에너지연구본부 수석연구원
4한국건설기술연구원 건축연구본부 수석연구원
*Corresponding Author
30 October 2025. pp. 262-272
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Abstract

Causal inference is emerging as an essential framework for advancing evidence-based decision-making in the building energy sector. This study analyzes the causal effect of building vintage on heating and cooling energy use in 762 office buildings in South Korea. Building vintage was defined as a proxy for improvements in insulation, equipment efficiency, and regulatory standards. The analysis employed the Causal Forest Double Machine Learning (CausalForestDML) method to estimate both average treatment effects (ATE) and conditional average treatment effects (CATE), while controlling for floor area, climate conditions, and heating systems. The results show that, unlike correlation-based analysis, causal inference explicitly reveals the influence of confounding factors. Moreover, the estimated effects of building vintage diverged between heating and cooling: heating use generally declined in newer buildings but increased in small ones, whereas cooling use tended to rise in newer medium- and large-sized buildings across most regions. These heterogeneous effects indicate that the performance improvements of new office buildings are not uniform and underscore the need for tailored strategies that account for building size, system type, and regional context. Overall, this study demonstrates that causal inference not only addresses the limitations of correlation-based approaches but also provides a reliable framework for supporting evidence-based policy design and performance improvement in the building energy domain.

Keywords
Building energy analysis
Evidence-based
Correlation analysis
Causal inference
Building vintage
키워드
건물 에너지 분석
증거기반
상관분석
인과추론
건물 노후도
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Information
  • Publisher :Korean Institute of Architectural Sustainable Environment and Building Systems
  • Publisher(Ko) :한국건축친환경설비학회
  • Journal Title :Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
  • Journal Title(Ko) :한국건축친환경설비학회논문집
  • Volume : 19
  • No :5
  • Pages :262-272
  • Received Date : 2025-09-19
  • Revised Date : 2025-10-15
  • Accepted Date : 2025-10-20
  • DOI :https://doi.org/10.22696/jkiaebs.20250023
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