电机与控制应用2023，Vol.50Issue(12)：42-53,12.DOI:10.12177/emca.2023.143
基于ICEEMDAN-SE-MSGJO-LSTM-EC 的短期风电功率预测
Short-Term Wind Power Prediction Based on ICEEMDAN-SE-MSGJO-LSTM-EC
摘要
Abstract
In order to improve the accuracy of short-term wind power prediction,this paper proposes a short-term wind power prediction model based on the ICEEMDAN-SE-MSGJO-LSTM-EC model.Firstly,the original wind power signal is decomposed using intrinsic computing expressive empirical mode decomposition with adaptive noise(ICEEMDAN)and reconstructed by adding components with similar entropy values through sample entropy calculation;Secondly,establish an multi-strategy golden jackal optimization(MSGJO)-long short-term memory network(LSTM)prediction model,optimize the LSTM network parameters through the improved MSGJO,and predict various modal components;Finally,error correction(EC)is applied to the prediction results of each modal component and all modal prediction results are added to obtain the final prediction result.Taking a wind farm in Xinjiang as an example,simulation analysis was conducted using the prediction model proposed in this paper.The experimental results showed that the prediction model based on ICEEMDAN-SE-MSGJO-LSTM-EC proposed in this paper has higher prediction accuracy.关键词
风电功率预测/误差修正/改进自适应噪声完全集合经验模态分解/改进金豺优化算法/长短期记忆网络Key words
wind power prediction/error correction/intrinsic computing expressive empirical mode decomposition with adaptive noise(ICEEMDAN)/multi-strategy golden jackal optimization(MSGJO)/long short-term memory network(LSTM)分类
信息技术与安全科学引用本文复制引用
刘志坚,孙瑞星,黄建,张江云,何超..基于ICEEMDAN-SE-MSGJO-LSTM-EC 的短期风电功率预测[J].电机与控制应用,2023,50(12):42-53,12.基金项目
云南省基础研究重点项目(202301AS070055)Key Basic Research Projects in Yunnan Province(202301AS070055) （202301AS070055）
