Open AccessChaotic characteristics analysis and prediction for short-term wind speed time series
Author(s) -
Zhongda Tian,
Shujiang Li,
Yanhong Wang,
Xianwen Gao
Publication year - 2015
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.64.030506
Subject(s) - series (stratigraphy) , chaotic , computer science , particle swarm optimization , time series , term (time) , algorithm , wind speed , embedding , control theory (sociology) , phase space , dimension (graph theory) , mathematics , artificial intelligence , machine learning , physics , meteorology , paleontology , control (management) , quantum mechanics , pure mathematics , biology , thermodynamics
A short-term wind speed time series prediction is studied. First, 0-1 test method for chaos is used to identify the short-term wind speed time series that has chaotic characteristics. Through phase space reconstruction, the delay time is determined by using C-C algorithm; and the embedding dimension is determined by using G-P algorithm. Then a least square support vector machine with parameters online modified is proposed, so that an improved particle swarm optimization algorithm may be used for the prediction of parameters optimization. Simulation experiment shows that the present method for its prediction accuracy, prediction error, and prediction effect is better than other prediction methods. Thus the proposed prediction method is effective, and feasible.