인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Intelligent home systems are increasingly being implemented in multi-family housing to improve residential energy management, although empirical evidence on their cost-saving effects at the building scale remains limited. This study evaluates how different levels of technological sophistication, including home network systems (G1), intelligent home network systems (G2), and no home network system (G3), on energy-related housing management costs. Multiple regression analyses are conducted using heating, hot water, gas, electricity, and water costs as dependent variables. The results show differentiated effects across energy categories. G2 is associated with significant reductions in heating and gas costs, suggesting that automated monitoring and control functions improve energy efficiency. Hot water costs decrease in G1, reflecting the routine nature of hot water usage, which can be efficiently managed through basic home network functions. Electricity costs increase in both G1 and G2, likely due to behavioral variability, increased use of electronic appliances, and additional power demand from system operations and common-area facilities. These findings indicate that cost-saving effects vary by energy type and level of system automation. This highlights the need for intelligent home systems to incorporate adaptive and context-aware control mechanisms to achieve broader efficiency gains.
본문·목차
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