메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
전재범 (서울과학기술대학교) 김영일 (서울과학기술대학교)
저널정보
대한설비공학회 설비공학논문집 설비공학논문집 제37권 제3호
발행연도
2025.3
수록면
143 - 150 (8page)
DOI
10.6110/KJACR.2025.37.3.143

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
The Building Energy Management System (BEMS) continuously monitors and analyzes real-time energy consumption data such as electricity and gas to optimize efficiency and propose energy-saving strategies. However, communication failures, device malfunctions, and system updates can lead to missing or abnormal data, necessitating effective data recovery methods. Addressing these data gaps is crucial for maintaining reliability of energy analysis. Traditionally, simple linear interpolation has been used for data recovery. However, this method is prone to substantial inaccuracies. In addition, it is insufficient for complexities of energy systems. The power consumption of a building is heavily influenced by heating and cooling loads, which are closely related to outdoor conditions. To restore missing power consumption data, we proposed a method that could identify a day with similar changes in outdoor enthalpy variation - a function of temperature and humidity - as the day with missing data. Power consumption pattern from this similar day was then used to reconstruct lost data. The efficacy of the proposed method was evaluated using CV-RMSE (Coefficient of variation of root mean square error), demonstrating a high degree of accuracy, with recovered data showing a deviation of just 3.1% from actual values. This enhanced methodology not only can improve data recovery accuracy, but also can reinforce robustness of energy consumption analysis, thereby supporting more reliable energy management decisions.

목차

Abstract
1. 서론
2. 연구방법
3. 실험 결과와 복원데이터 비교
4. 결론
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-151-25-02-092457316