Open AccessWind energy prediction with LS‐SVM based on Lorenz perturbation
Author(s) -
Zhang Yagang,
Wang Penghui,
Zhang Chenhong,
Lei Shuang
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0626
Subject(s) - wind speed , wind power , control theory (sociology) , computer science , renewable energy , perturbation (astronomy) , lorenz system , support vector machine , aerodynamics , artificial neural network , chaotic , meteorology , artificial intelligence , engineering , physics , aerospace engineering , control (management) , quantum mechanics , electrical engineering
Owing to the deterioration of the global environment and the depletion of traditional resources, renewable energy has received a high degree of attention. Among them, the fastest growing wind energy has become an excellent alternative to traditional energies. But the non‐linearity and volatility of wind speed have brought great challenges to the stability of power system. As a deterministic system, the motion of an aerodynamic system can be described as a set of simple differential equations – Lorenz equation. Thus a small disturbance in the system will have a great impact on the formation of the wind and the wind power prediction work. Therefore, on consideration of the atmospheric dynamical system, a least squares support vector machine (LS‐SVM) wind speed prediction model based on Lorenz perturbation is proposed here. The results show that compared with the traditional prediction model (LS‐SVM, RBF neural network), the model proposed in this paper effectively weakens the fluctuation of the wind speed sequence and significantly improves the accuracy of short‐term wind speed prediction. The research work of this study will reduce the electric running cost and can effectively promote the large‐scale development and utilization of the renewable energy.