## 1 Introduction
### Introduction of the Proposed Model  
However, these individual models mentioned above have certain drawbacks. The disadvantages are summarized as followed: (1) They usually require a huge amount of wind speed data in order to build a model for a precise prediction, because of the inner irregularity and instability within the raw data. If the raw data suddenly changes due to environmental factors, the error of the forecasting result can be relatively high [23]; (2) Some models only try to match the fitness of the model closer to the original data. As a result, when processing the wind speed data with high noise and irregularity, it is difficult to fit the individual model to it by using only traditional physical or statistical methods, causing poor forecasting accuracy and low efficiency; (3) Individual forecasting methods ignore the importance of data preprocessing and the necessity of model parameter optimization. Because of this, the accuracy of the result is not satisfying; (4) While some new models take advantage of artificial intelligence to enhance their forecasting ability, they still have the problem of over-fitting and a low convergence rate [24].  
Therefore, in order to solve the problems mentioned above, a new hybrid model for more precise wind speed forecasting and better evaluation, achieved by adapting the Ensemble Empirical Mode Decomposition (EEMD), the Cuckoo Search Optimization (CSO) algorithm, and the Support Vector Machine (SVM) model, is introduced in this paper.  
The main contributions and innovations of this paper compared to other studies in the field of wind speed forecasting are summed up as follows:  
* The newly proposed approach in this paper takes advantage of the data preprocessing method and the algorithm of parameter optimization to enhance the performance of the SVM model. In this paper, the raw time series is first decomposed into several sub-signals, among which signals with high frequency ones are removed and the rest are restructured to obtain a stationary time series, with which the intrinsic characteristics of the wind speed data can be better captured and analyzed so that the forecasting accuracy can be greatly improved.
* This paper employs the Cuckoo Search algorithm to optimize the parameters of the SVM before training. The CS algorithm, which has the advantage of a powerful capability in terms of global optimization, requires few parameters and has strong multi-objective problem solving ability, and therefore can significantly improve the accuracy of the forecasting. The Support Vector Machine can overcome the difficulties of traditional models, such as the curse of dimensionality, falling into local optima easily, and over-learning.
* To verify the forecasting ability of the proposed approach, conventional models like BPNN, RBFNN, and ARIMA are used for comparison. A more comprehensive evaluation is conducted, including multi-step forecasting experiments and performance evaluation metrics such as six indexes and a DM(Diebold-Mariano) test, to assess and analyze the performance of the newly developed method.  
The model used the raw data from wind turbines in a large wind farm. The result of this paper shows that the newly developed approach effectively improves the forecasting accuracy and can be successfully applied in wind grids to provide statistical support for making operation plans and managing the power station.