电网技术2023，Vol.47Issue(12)：5015-5025,11.DOI:10.13335/j.1000-3673.pst.2022.1367
基于CEEMDAN-PE-WPD和多目标优化的超短期风电功率预测方法
Ultra-short-term Wind Power Prediction Based on CEEMDAN-PE-WPD and Multi-objective Optimization
摘要
Abstract
In order to improve the accuracy of wind power prediction,an ultra-short-term wind power prediction based on the overall average empirical mode decomposition(CEEMDAN),the Permutation Entropy(PE),the Wavelet Packet Decomposition(WPD)and the multi-objective optimization is proposed.First,the signal processing method consisting of the CEEMDAN,the PE and the WPD is used to reduce the randomness and volatility of the original wind power signals;then,the decomposed subcomponents are fed into the Long/Short-Term Memory(LSTM)network,and an improved Elite T-distribution Sparrow Optimization Algorithm(ETSSA)is used to optimize the number of hidden layer units of the LSTM to improve the prediction performance of the LSTM network;finally,the loss function is optimized with the three optimization objectives of accuracy,stability and pass rate added into it at the same time to improve the prediction performance of the model comprehensively.The experimental analysis of the measured data from a wind farm in a region in Inner Mongolia shows that,compared with other classical prediction methods,the proposed method has a more significant effect on improving the wind power prediction performance and a better prediction effect under different seasonal wind conditions.关键词
超短期风电功率预测/总体平均经验模态分解/排列熵/小波包分解/长短期记忆神经/精英T分布麻雀优化算法/多目标优化分类
信息技术与安全科学引用本文复制引用
常雨芳,杨子潇,潘风,唐杨,黄文聪..基于CEEMDAN-PE-WPD和多目标优化的超短期风电功率预测方法[J].电网技术,2023,47(12):5015-5025,11.基金项目
国家自然科学基金项目(61903129) （61903129）
湖北工业大学博士启动基金研究项目(BSQD2020012).Project Supported by National Natural Science Foundation of China(61903129) （BSQD2020012）
Doctoral Startup Fund Research Project of Hubei University of Technology(BSQD2020012). （BSQD2020012）
