基于SMD与WaOA-CNN-LSTM的短期光伏功率预测  

Short-term photovoltaic power prediction based on SMD and WaOA-CNN-LSTM

作　　者：武文珍[1] 毛伟进 WU Wenzhen;MAO Weijin(School of Business,Shanghai Dianji University,Shanghai 201306,China)

机构地区：[1]上海电机学院商学院,上海201306

出　　处：《上海电机学院学报》2024年第5期292-298,共7页Journal of Shanghai Dianji University

摘　　要：针对当前光伏功率预测模型所面临因数据的复杂性、信号处理过程的噪声干扰、非线性特征难以提取等问题而导致的预测精度低、稳定性差等多方面挑战,提出了一种融合二次模态分解(SMD)和基于海象算法(WaOA)优化CNN-LSTM神经网络的组合预测模型。首先,利用完全自适应噪声集合经验模态分解(CEEMDAN)对光伏数据进行分解,并结合K均值聚类算法(K-means)将多个子序列重构成低频、中频以及高频序列;其次,将含有残余噪声的高频序列采用变分模态分解(VMD)进行二次分解处理;最后,对各分量分别构建CNN-LSTM模型,并利用WaOA算法对网络参数进行寻优,将各分量的预测结果进行叠加,得到最终预测结果。SMD处理方法解决了传统数据处理方法模态混叠、低频分量过多和高频分量噪声残余等问题,CNN-LSTM模型能够捕捉数据中的空间关系和长期依赖关系,WaOA算法对模型参数的优化提高了模型的性能和效率。选取陕西某地光伏电站数据进行测试,通过多组对比实验进行验证,结果表明:所提方法具有更高的预测精度。To address the challenges of low prediction accuracy and poor stability caused by the complexity of data,noise interference in signal processing,and difficulty in extracting nonlinear features in current photovoltaic power prediction models,a hybrid prediction model combining second-order mode decomposition(SMD)and CNN-LSTM neural network optimized by Walrus Optimization Algorithm(WaOA)is proposed.Firstly,the photovoltaic data is decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and multiple sub-sequences are reconstructed into low-frequency,medium-frequency,and high-frequency sequences by combining with K-means clustering algorithm(K-means).Secondly,the high-frequency sequences containing residuals are subjected to a second-order decomposition using Variational Mode Decomposition(VMD).Finally,CNN-LSTM models are constructed for each component separately.The prediction results of each component are then superimposed to obtain the final prediction result.The SMD processing method addresses the issues of mode aliasing,excessive low-frequency components,and residual noise in high-frequency components commonly found in traditional data processing methods.The CNN-LSTM model can capture spatial relationships and long-term dependencies in the data,while the WaOA algorithm improves the model performance and efficiency by optimizing the model parameters.The experimental results on the data from a photovoltaic power station in Shaanxi Province demonstrate the higher prediction accuracy of the proposed method through multiple comparative experiments.

关 键 词：二次模态分解 短期光伏功率预测 海象优化算法 深度学习 

分 类 号：TM615[电气工程—电力系统及自动化]