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
This study examined the possibility of developing a time-series-based electricity demand response model by applying a VSM data set model generated from 30,000 household on BSEM in Quebec, Canada, to the demand response of the domestic electricity market. The CBL method, which has been adopted as a conventional demand forecasting method, has limitations in sufficiently reflecting the nonlinearity of maximum power demand and demand response because it calculates based on the average of past power consumption data. Therefore, in this study, the structure of the VSM data set which generated BSEM and the TRNSYS-CREST simulation were analyzed, and the applicability of the demand response was evaluated through the verification of the LSTM, GRU, and CNN-LSTM models using the VSM data set. As the results , the LSTM and GRU models showed slightly higher error index than the CBL, while the CNN-LSTM structure effectively predicted short-term fluctuations through convolutional features combined with time dependency. This confirmed the possibility of more precise DR if the conventional CBL method and the AI prediction model were combined. In conclusion, this study confirmed the possibility of constructing a Korean VSM data set that reflects institutional factors such as BSEM-based energy prediction, TOU rate system, and DR, focusing on apartment complexes in Korea, and the possibility of follow-up research on simulation and empirical models based on RSM data.
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
UCI(KEPA) : I410-151-26-02-094278550
