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
Abstract Wind power energy is green, clean, and renewable, which is random and volatile. The integration of unstable wind energy severely threatens the security and constant operation of the power system. The need to enhance the reliability of wind power grid integration, mitigate the impact of wind power uncertainty, and develop a robust prediction model has become a pressing issue. However, only some people have considered the correlations among the power of multiple adjacent wind turbine arrays. In this paper, we propose GCNInformer to construct these relationships. Furthermore, we analyze the relationships among multiple features of individual wind turbines. GCNInformer is composed of two main components. The first component employs a graph convolutional network (GCN) to establish relationships among multiple wind turbine arrays, enhancing the correlation of the data. The second part employs Informer to extract the time information from the data and predict long‐term sequences. For training and testing, GCNInformer utilizes two data sets: Data_CQ and Data_DL. The evaluation of the model's performance is conducted using various metrics such as mean absolute percentage error, mean absolute error, root mean square error, and mean square error. Numerous experimental findings have validated the effectiveness of the GCNInformer.
#Wind power
#Mean absolute percentage error
#Mean squared error
#Computer science
#Renewable energy
#Turbine
#Power (physics)
#Wind power forecasting
#Random forest
#Reliability (semiconductor)
#Electric power system
#Simulation
#Reliability engineering
#Data mining
#Artificial intelligence
#Statistics
#Mathematics
#Engineering
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