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
Personal slow chargers account for 83.4 % of all EV chargers on Jeju Island, yet their hourly charging behavior is unobservable because these units have no communication capability and utility metering records only monthly energy by time of use (TOU) period. This paper presents a field measurement based framework that estimates their hourly charging profile and expands it toward the population. From nine months of charging protocol logs and metering records of five 7 kW class residential chargers, 832 sessions and 5-minute power trajectories were reconstructed. The reconstruction was verified along three independent paths: monthly aggregates matched utility meter readings; simultaneous inlet outlet measurement showed a 0.54 % mean deviation; and the resulting TOU composition agreed with metered records within 0.5 percentage points. Leave one out cross-validation, in which each unit was estimated from the most similar remaining unit, yielded an energy weighted mean absolute error of 2.70 percentage points. Using hourly allocation ratios derived by season and day type, 3 036 chargers with similar TOU composition were selected from 15 984 quality filtered candidates and their monthly energies decomposed into hourly profiles while preserving monthly totals. Both the measured units and the expanded group show late night concentration, with 62.2 % and 77.9 % of energy consumed between 22:00 and 06:00, while only 6.8 % falls within the daytime hours when photovoltaic surplus arises. The results provide an hourly demand basis for identifying chargers that could absorb surplus generation without hardware replacement.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
