머신러닝 기술을 활용한 건축물 사례기반 온실가스 감축 의사결정 지원 모델 구축

Abstract

Purpose: Many countries have implemented policies to reduce greenhouse gas (GHG) emissions in publicbuildings, emphasizing the leading role of the public sector. In Korea, in order to achieve a 30% reduction in GHGemissions by 2030, public agencies must set annual targets or quotas. However, the lack of experts and support arethe biggest obstacles to achieving this reduction target. Methods: This study constructed a GHG evaluation database(DB) and Data set based on energy end uses, GHG reduction technology with the aim of decision making aboutGHG reduction with minimal building information and limited expert knowledge. The GHG evaluation DB wasbuilt using data from the Commercial Building Energy Consumption Survey (CBECS), an energy consumptionsurvey of 6,720 public and commercial buildings by the US Department of Energy. In addition, a DB for evaluatingthe reduction amount of greenhouse gas reduction technology was established with reference to 1,206 greenhousegas reduction technology application projects by the Korea Energy Survey. The database was used for constructingdata set, we developed a machine learning-based GHG reduction decision support model. Result: Additionally, thecase study of domestic public buildings, the economic and environmental benefit of applying greenhouse gasreduction technology were evaluated. The evaluated building can reduce about 111 tonCO2-eq and convert it intoeconomic profit of 36 million won, confirming the applicability of the model.