Assessment of Wind Power Prediction Using Hybrid Method and Comparison with Different Models

作者：Eissa, Mohammed^*; Yu Jilai; Wang Songyan; Peng Liu

来源：Journal of Electrical Engineering and Technology, 2018, 13(3): 1089-1098.

DOI：10.5370/JEET.2018.13.3.1089

• Wind power prediction
• Hybrid
• ARMA
• SVM
• ANN
• Error

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR& LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.

• 出版日期2018-5
• 单位哈尔滨工业大学