信息与控制2016，Vol.45Issue(2)：135-141,7.DOI:10.13976/j.cnki.xk.2016.0135
基于CEEMDAN-FE-KELM方法的短期风电功率预测
Short-term Wind Power Forecasting Based on CEEMDAN-FE-KELM Method
摘要
Abstract
Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-fuzzy en-tropy (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 or-der 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 sub-sequences 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 ap-plied the proposed CEEMDAN-FE-KELMmethod 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.关键词
集成经验模态分解/核极限学习机/模糊熵/组合方法/风电功率预测Key words
ensemble empirical mode decomposition/extreme learning machine with kernels/fuzzy entropy/combined model/wind power forecasting分类
信息技术与安全科学引用本文复制引用
李军,李大超..基于CEEMDAN-FE-KELM方法的短期风电功率预测[J].信息与控制,2016,45(2):135-141,7.基金项目
国家自然科学基金资助项目 （）
