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| Content Provider | IET Digital Library |
|---|---|
| Author | Zhang, Yagang Wang, Penghui Zhang, Chenhong Lei, Shuang |
| Abstract | 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. |
| Starting Page | 1724 |
| Ending Page | 1727 |
| Page Count | 4 |
| Volume Number | 2017 |
| e-ISSN | 20513305 |
| Issue Number | Issue 13, Jan (2017) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2017/13 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2017.0626 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2017-01-01 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Aerodynamic System Atmospheric Dynamical System Atmospheric Power System Chaos Deterministic System Global Energy Transformation A Good Opportunity Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Least Squares Approximation Lorenz Equation Lorenz Perturbation LS-SVM Nonlinear Characteristic Nonlinear Disturbance Factors Nonlinear Process Numerical Analysis Power Engineering Computing Renewable Energy Short-term Wind Speed Prediction Support Vector Machine Wind Energy Prediction Wind Farm Wind Power Industry Wind Power Plant Wind Speed Prediction Model Wind Speed Sequence |
| Content Type | Text |
| Resource Type | Article |
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