风速时间序列的符号化描述

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. © 2017 Automation of Electric Power Systems Press.