基于时间序列特征的“离线分类优化、在线模型匹配”超短期风电预测方法

Q1 Engineering

电力系统自动化 Pub Date : 2015-04-25 DOI:10.7500/AEPS20141230007

Chenhui Yu, Yusheng Xue, F. Wen, Z. Dong, K. Wong, Kang Li

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引用次数: 8

摘要

综述了超短期风电预测模型的适用性。该方法从风力发电时间序列(WPTS)的历史数据中提取特征，并将每个短WPTS分类到由平稳模式定义的几个不同子集中的一个。所有不能匹配任何一种平稳模式的WPTS被分类到非平稳模式的子集中。以上每个WPTS子集都需要一个专门为其离线优化的USTWPP模型。在在线应用中，首先识别出最后一个短WPTS的模式，然后调用相应的USTWPP预测模型。仿真结果验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An Ultra-short-term Wind Power Prediction Method Using "Offline Classification and Optimization,Online Model Matching" Based on Time Series Features

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.

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来源期刊

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.