宁夏电力Issue(6)：42-48,7.DOI:10.3969/j.issn.1672-3643.2016.06.008
基于排列熵重构的EEMD-RVM短期光伏功率预测
EEMD-RVM short-term photovoltaic power forecasting based on permutation entropy reconstruction
林翔 1武小梅 1谢海波 1谢旭泉1
作者信息
- 1. 广东工业大学自动化学院，广东 广州 510006
- 折叠
摘要
Abstract
It’s hard to accurately forecast the periodicity of the photovoltaic power series due to its non-stationarity, puts forward a novel short-term photovoltaic power forecasting approach as ensemble empirical mode decomposition (EEMD) and relevance vector machine (RVM) based on permutation entropy reconstruction. The simulation results illustrate that the method can not only improve the accuracy of short-term photovoltaic power forecasting, but also shorten the forecasting time, and promote the efficiency of photovoltaic power forecasting. The method is suitable for photovoltaic power short-term on-line forecasting.关键词
集合经验模态分解/排列熵/相关向量机/光伏功率预测Key words
ensemble empirical mode decomposition (EEMD)/permutation entropy/relevance vector machine (RVM)/photovoltaic power forecast分类
信息技术与安全科学引用本文复制引用
林翔,武小梅,谢海波,谢旭泉..基于排列熵重构的EEMD-RVM短期光伏功率预测[J].宁夏电力,2016,(6):42-48,7.
