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.
초록·키워드
Data scarcity and high development costs pose significant challenges to building-specific energy demand forecasting models. To address these issues, this study introduces a time series similarity assessment method that utilizes TS2Vec, an unsupervised learning-based encoder for extracting time series representation vectors. The efficacy of this approach is demonstrated using anonymized datasets of building electricity usage from Cambridge, UK. The proposed methodology stands out for its ability to identify high-similarity data segments by flexibly adjusting the evaluation time window used for extracting representation vectors, outperforming traditional average similarity assessments. Principal component analysis was employed for dimensionality reduction and visualization, alongside a moving window cosine similarity approach to enhance the interpretability of complex multivariate time series data similarities. The study's key findings are as follows. First, dynamic similarity analysis effectively captured the complexity of building energy use patterns. Second, the approach demonstrated the potential to optimize transfer learning by automatically identifying the most suitable source data. Third, the study explored the feasibility of employing dynamic model selection and ensemble techniques based on temporal similarity changes. This study proposes a practical and scalable methodology to mitigate data scarcity and reduce model development costs, thereby facilitating more efficient, adaptive, and accurate energy demand forecasting.
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
