Chenhui Yu, Yusheng Xue, F. Wen, Z. Dong, K. Wong, Kang Li
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引用次数: 8
Abstract
The applicability of ultra-short-term wind power prediction(USTWPP)models is reviewed.The USTWPP method proposed extracts featrues from historical data of wind power time series(WPTS),and classifies every short WPTS into one of several different subsets well defined by stationary patterns.All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern.Every above WPTS subset needs a USTWPP model specially optimized for it offline.For on-line application,the pattern of the last short WPTS is recognized,then the corresponding prediction model is called for USTWPP.The validity of the proposed method is verified by simulations.
电力系统自动化Energy-Energy Engineering and Power Technology
CiteScore
8.20
自引率
0.00%
发文量
15032
期刊介绍：
Founded in 1977, Power System Automation is a well-known journal in the discipline of electrical engineering in China. At present, it has been issued to all provinces, cities, autonomous regions, Hong Kong, Macao and Taiwan, and abroad to dozens of countries in North America, Europe and Asia-Pacific region, with a large number of readers at home and abroad. Power System Automation takes “based on China, facing the world, seeking truth and innovation, promoting scientific and technological progress in the field of electric power and energy” as the purpose of the journal, mainly for the professional and technical personnel, teachers and students engaged in scientific research, design, operation, testing, manufacturing, management and marketing in the electric power industry and higher education institutions as well as electric power users, and focuses on hotspots of the industry's development and the It focuses on the hot and difficult issues of the industry. It focuses on the hot and difficult issues of the industry, both academic and forward-looking, practical and oriented, and at the same time emphasizes and encourages technical exchanges of experiences, improvements and innovations from the front line of scientific research and production.
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