风速时间序列的符号化描述

利用统计分析技术,对已知的时间序列外推,可以克服短期风速预测中缺乏因果关系的困难。但在选择外推模型、参数及学习样本等方面存在主观认识模糊性的挑战。为降低主观认识模糊性对分类预测效果的影响并提高样本分类效率,提出按变化特征来定义符号,以及用符号串描述风速时间序列的粗粒化概念。在此基础上,引入趋势特征,完善风速时间序列的符号化过程,提出单元窗口特征和趋势特征相结合的两层符号化方法。利用甘肃酒泉风电基地一年的实际数据验证了该粗粒化方法的有效性。

Symbolizing for Wind Speed Time Series

By using statistical analysis technique, the difficulty of lacking causality in short-term wind speed prediction can be overcome by extrapolating the known time series. However, because of the fuzziness of subjective cognition, challenges exist in the choice of extrapolation models, parameters and training samples. To reduce the influences of fuzziness of subjective cognition on the performance of classification prediction and improve the efficiency of sample classification, the concept of coarseness of wind speed time series(WSTS)is proposed. Symbols defined according to tendency features are used to describe WSTS. Based on this, a two-layer symbolizing method using unit window feature and variation trend feature is proposed to improve WSTS symbolization. Finally, a case study based one year data collected from a wind farm at Jiuquan wind power base in Gansu Province is presented to validate the effectiveness of the proposed coarseness method.