基于LSTM和粒子群算法的多机组风电功率预测  

Multi-unit Wind Power Prediction Based on Long Short-term Memory andParticle SwarmOptimization

作　　者：吴振龙 莫艺鹏 王荣花[2] 范鑫雨 刘艳红[1] 郭小联 WU Zhenlong;MO Yipeng;WANG Ronghua;FAN Xinyu;LIU Yanhong;GUO Xiaolian(School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Department of Electrics and Automation,Shandong Labor Vocational and Technical College,Jinan 250300,China;Zhejiang Academy of Special Equipment Science,Hangzhou 310020,China)

机构地区：[1]郑州大学电气与信息工程学院,河南郑州450001 [2]山东劳动职业技术学院电气及自动化系,山东济南250300 [3]浙江省特种设备科学研究院,浙江杭州310020

出　　处：《郑州大学学报（工学版）》2024年第6期114-121,共8页Journal of Zhengzhou University（Engineering Science）

基　　金：国家自然科学基金资助项目(52106030);电力系统国家重点实验室开放课题(SKLD21KM14);郑州大学教授团队助力企业创新驱动发展专项(JSZLQY2022016);郑州大学青年人才企业合作创新团队支持计划。

摘　　要：目前,风电功率预测所使用的模型想要达到预测效果,需要对模型选择合适的超参数,但手动调参数时间成本大、可信度较低。基于此,提出了一种基于长短期记忆网络(LSTM)的多机组风电功率预测方法。首先,采用斯皮尔曼相关系数法对数据进行量化分析;其次,运用主成分分析对输入特征进行降维,提取关键信息。除此之外,针对LSTM调参困难这一问题,采用粒子群算法对LSTM每层隐含层神经元的个数进行优化。对于多机组的风电功率预测问题,以单机组为切入点,找出单机组中表现最为优异的模型,将该预测模型应用至多机组预测。实验结果表明:与其他模型相比,所提方法均方根误差下降了11.8%,平均绝对误差下降了5.03%。At present,the manual adjustment of hyper-parameter for current wind power prediction model was slow and unreliability.In order to achieve the prediction effect,the model used in wind power prediction needs to select the appropriate hyper-parameters for the model.Based on this,in this study,a multi-unit wind power prediction model was proposed based on long short-term memory(LSTM).Firstly,the Spearman correlation method was used to quantitative analysis.Secondly,the principal component analysis(PCA)was used to reduce the dimension of the input features as well as extract the key information.In addition,considering the difficulty of choosing parameters for LSTM,in this study,particle swarm optimization(PSO)algorithm was used to optimize the number of hidden layer neurons in each layer of LSTM.For the problem of wind power prediction of multiple units,in this study,a single wind turbine was used to find the most excellent model in a single unit,and applied the prediction model to multi-unit prediction.Experiments showed that compared with other models,the root mean square error of the proposed method was reduced by 11.8%,and the mean absolute error was reduced by 5.03%.

关 键 词：长短期记忆网络 风电功率预测 多机组 粒子群优化算法 特征选择 

分 类 号：TM614[电气工程—电力系统及自动化] TK81[动力工程及工程热物理—流体机械及工程]