基于CEEMDAN-FE-KELM方法的短期风电功率预测OA北大核心CSCDCSTPCD
Short-term Wind Power Forecasting Based on CEEMDAN-FE-KELM Method
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…查看全部>>
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…查看全部>>
李军;李大超
兰州交通大学自动化与电气工程学院，甘肃兰州 730070兰州交通大学自动化与电气工程学院，甘肃兰州 730070
信息技术与安全科学
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
ensemble empirical mode decompositionextreme learning machine with kernelsfuzzy entropycombined modelwind power forecasting
《信息与控制》 2016 (2)
基于高斯过程的短期风电功率概率预测方法研究
135-141,7
国家自然科学基金资助项目（51467008）
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