基于CEEMDAN-FE-KELM方法的短期风电功率预测

针对短期风电功率预测，提出一种基于自适应噪声完整集成经验模态分解（complete ensemble empirical mode decomposition with adaptive noise，CEEMDAN）-模糊熵（FE）的核极限学习机（extreme learning machine with kernels，KELM）组合预测方法．CEEMDAN方法在信号分解的每一阶段都添加特定的白噪声，通过计算唯一的余量信号以获取各个模态分量，与EEMD（ensemble empirical mode decomposition）方法相比，其分解过程是完整的．为降低信号非平稳性对预测精度的影响及减少计算规模，采用CEEMDAN-模糊熵（FE）方法将信号分解为具有不同复杂度差异的子序列，然后分别构建相应的KELM预测模型，最后对预测结果进行合成．将CEEMDAN-FE-KELM方法应用于某地区的短期风电功率预测，在同等条件下，与单一的KELM方法及KELM的组合预测方法进行实验对比，结果证明该方法更有效．

Short-term Wind Power Forecasting Based on CEEMDAN-FE-KELM Method

Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-fuzzy entropy (FE) and an extreme learning machine with kernels (KELM), we propose a combined forecasting method for short-term wind power forecasting. The CEEMDAN method adds a particular white noise at each stage of the decomposition and computes a unique residue to obtain each stage's intrinsic model function (IMF). Compared with the EEMD method, the decomposition process of the CEEMDAN is complete. In order to weaken the influence of the signal's non-stationary effects on the prediction accuracy and to reduce the computational scale, we use the CEEMDAN-FE method to decompose the original signal into a series of subsequences with obvious differences in their degree of complexity. Then, we build the corresponding KELM forecasting model. Finally, we combine these forecasting results to output the final forecasting result. We applied the proposed CEEMDAN-FE-KELM method to a short-term wind power forecasting situation in one area. Under the same conditions, a comparison of the results using the single KELM method with those from the combined KELM-based forecast model shows the proposed method to be more effective.